{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## load pacakges\n",
    "push!(LOAD_PATH, pwd()) # add the current folder, which contains Utils.jl, to LOAD_PATH\n",
    "using Utils, DataFrames, PyPlot, NLsolve, Optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## matplotlib settings\n",
    "#  not important, only to write latex on graphs\n",
    "fontsize = 16; fonttype = \"serif\"\n",
    "# fonttype = \"sansserif\"\n",
    "PyPlot.matplotlib.rc(\"text\", usetex=true) # allow tex rendering\n",
    "PyPlot.matplotlib.rcParams[\"text.latex.unicode\"] = true\n",
    "if fonttype==\"serif\" # use serif math font\n",
    "    PyPlot.matplotlib.rc(\"font\", family=\"serif\", serif=\"Times\", size=16)\n",
    "    #PyPlot.matplotlib.rc(\"text.latex\",preamble=\"\\\\usepackage[libertine]{newtxmath}\")\n",
    "else # use sans serif math font\n",
    "    PyPlot.matplotlib.rc(\"font\", family=\"sans-serif\", size=16)\n",
    "    PyPlot.matplotlib.rc(\"text.latex\",preamble=\"\\\\usepackage{newtxsf}\")\n",
    "end\n",
    "PyPlot.matplotlib.rcParams[\"text.latex.unicode\"] = false;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "solveDemographics (generic function with 1 method)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function solveDemographics(diPara,θLO,θHN;absorbingBounds=false)\n",
    "    if absorbingBounds\n",
    "        θLO = maximum([0.0,minimum([1.0,θLO])])\n",
    "        θHN = maximum([0.0,minimum([1.0,θHN])])\n",
    "    end\n",
    "    ρB, nB, qB = diPara[:searchB]; ρS, nS, qS = diPara[:searchS]\n",
    "    λu, λd, mD, s = [diPara[vrb] for vrb in [:λu,:λd,:mD,:s,]]\n",
    "    η = λu/(λu + λd)\n",
    "    diX = Dict(); diB = Dict(); diS = Dict()\n",
    "    function resolveDemographicsX!(diB,diS,diX,x)\n",
    "        diB[:mLO] = transBetween(x[1],lower=0.0,upper=1.0)\n",
    "        diS[:mLO] = transBetween(x[2],lower=0.0,upper=1.0)\n",
    "        diX[:mDO] = transBetween(x[3],lower=0.0,upper=mD)\n",
    "        diB[:mHN] = transBetween(x[4],lower=0.0,upper=1.0)\n",
    "        diX[:mLN] = (1.0 - η) - diB[:mLO] - diS[:mLO]\n",
    "        diX[:mDN] = mD - diX[:mDO]\n",
    "        diX[:mHO] = s - diB[:mLO] - diS[:mLO] - diX[:mDO]\n",
    "        diS[:mHN] = η - diX[:mHO] - diB[:mHN]\n",
    "        diB[:νLO] = (1.0 - (1.0 - diX[:mDN]/mD)^nB)\n",
    "        diB[:νHN] = (1.0 - (1.0 - diX[:mDO]/mD)^nB)\n",
    "        diS[:νLO] = (1.0 - (1.0 - diX[:mDN]/mD)^nS)\n",
    "        diS[:νHN] = (1.0 - (1.0 - diX[:mDO]/mD)^nS)\n",
    "    end\n",
    "    function f!(fvec,x)\n",
    "        resolveDemographicsX!(diB,diS,diX,x)\n",
    "        fvec[1] = - λu*diS[:mLO] + λd*diX[:mHO]*θLO - ρS*diS[:νLO]*diS[:mLO]\n",
    "        fvec[2] = - λu*diB[:mLO] + λd*diX[:mHO]*(1.0 - θLO) - ρB*diB[:νLO]*diB[:mLO]\n",
    "        fvec[3] = - λd*diS[:mHN] + λu*diX[:mLN]*θHN - ρS*diS[:νHN]*diS[:mHN]\n",
    "        fvec[4] = (ρS*diS[:νLO]*diS[:mLO] + ρB*diB[:νLO]*diB[:mLO]) - (ρS*diS[:νHN]*diS[:mHN] + ρB*diB[:νHN]*diB[:mHN])\n",
    "    end\n",
    "    sol = nlsolve(f!,zeros(4))\n",
    "    converging = sol.x_converged | sol.f_converged\n",
    "    resolveDemographicsX!(diB,diS,diX,sol.zero)\n",
    "    return converging, diB, diS, diX\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "solveValueFunctions! (generic function with 1 method)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function solveValueFunctions!(diPara,diB,diS,diX,θLO,θHN;absorbingBounds=false)\n",
    "    if absorbingBounds\n",
    "        θLO = maximum([0.0,minimum([1.0,θLO])])\n",
    "        θHN = maximum([0.0,minimum([1.0,θHN])])\n",
    "    end\n",
    "\n",
    "    ρB, nB, qB = diPara[:searchB]; ρS, nS, qS = diPara[:searchS]; yh, yd, yl = diPara[:values]\n",
    "    λu, λd, r, mD = [diPara[vrb] for vrb in [:λu,:λd,:r,:mD,]]\n",
    "    for (di,ρ,n,q) in zip([diB,diS,],[ρB,ρS,],[nB,nS,],[qB,qS,])\n",
    "        di[:ABar] = n*diX[:mDO]/mD*(1.0 - diX[:mDO]/mD)^(n-1)/(1.0 - (1.0 - diX[:mDO]/mD)^n)\n",
    "        di[:BBar] = n*diX[:mDN]/mD*(1.0 - diX[:mDN]/mD)^(n-1)/(1.0 - (1.0 - diX[:mDN]/mD)^n)\n",
    "        di[:ζLO] = ρ*di[:νLO]*(q + (1.0 - q)*(1.0 - di[:BBar]))\n",
    "        di[:ζHN] = ρ*di[:νHN]*(q + (1.0 - q)*(1.0 - di[:ABar]))\n",
    "        di[:ζDO] = ρ*di[:νHN]*di[:mHN]/diX[:mDO]*(1.0 - q)*di[:ABar]\n",
    "        di[:ζDN] = ρ*di[:νLO]*di[:mLO]/diX[:mDN]*(1.0 - q)*di[:BBar]\n",
    "    end\n",
    "    function resolveValueX!(diB,diS,diX,x)\n",
    "        diX[:vHO],diX[:vLN],diX[:vDO],diX[:vDN],diB[:vHN],diB[:vLO],diS[:vHN],diS[:vLO] = x\n",
    "        diB[:ΔHN] = (diX[:vHO] - diB[:vHN]) - (diX[:vDO] - diX[:vDN])\n",
    "        diB[:ΔLO] = (diX[:vDO] - diX[:vDN]) - (diB[:vLO] - diX[:vLN])\n",
    "        diS[:ΔHN] = (diX[:vHO] - diS[:vHN]) - (diX[:vDO] - diX[:vDN])\n",
    "        diS[:ΔLO] = (diX[:vDO] - diX[:vDN]) - (diS[:vLO] - diX[:vLN])\n",
    "    end\n",
    "    function f!(fvec,x)\n",
    "        resolveValueX!(diB,diS,diX,x)\n",
    "        fvec[1] = yh + λd*(θLO*diS[:vLO] + (1.0 - θLO)*diB[:vLO] - diX[:vHO]) - r*diX[:vHO]\n",
    "        fvec[2] = λu*(θHN*diS[:vHN] + (1.0 - θHN)*diB[:vHN] - diX[:vLN]) - r*diX[:vLN]\n",
    "        fvec[3] = yd + diB[:ζDO]*diB[:ΔHN] + diS[:ζDO]*diS[:ΔHN] - r*diX[:vDO]\n",
    "        fvec[4] = diB[:ζDN]*diB[:ΔLO] + diS[:ζDN]*diS[:ΔLO] - r*diX[:vDN]\n",
    "        fvec[5] = λd*(diX[:vLN] - diB[:vHN]) + diB[:ζHN]*diB[:ΔHN] - r*diB[:vHN]\n",
    "        fvec[6] = λd*(diX[:vLN] - diS[:vHN]) + diS[:ζHN]*diS[:ΔHN] - r*diS[:vHN]\n",
    "        fvec[7] = yl + λu*(diX[:vHO] - diB[:vLO]) + diB[:ζLO]*diB[:ΔLO] - r*diB[:vLO]\n",
    "        fvec[8] = yl + λu*(diX[:vHO] - diS[:vLO]) + diS[:ζLO]*diS[:ΔLO] - r*diS[:vLO]\n",
    "    end\n",
    "    sol = nlsolve(f!,zeros(8))\n",
    "    converging = sol.x_converged | sol.f_converged    \n",
    "    resolveValueX!(diB,diS,diX,sol.zero)\n",
    "    return converging\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "marketQuality! (generic function with 1 method)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function marketQuality!(diPara,diB,diS,diX,θLO,θHN)\n",
    "    ρB, nB, qB = diPara[:searchB]; ρS, nS, qS = diPara[:searchS]; yh, yd, yl = diPara[:values]\n",
    "    λu, λd, r, mD = [diPara[vrb] for vrb in [:λu,:λd,:r,:mD,]]\n",
    "    \n",
    "    for (di,ρ,n,q) in zip([diB,diS,],[ρB,ρS,],[nB,nS,],[qB,qS,])\n",
    "        di[:volume] = ρ*di[:mLO]*di[:νLO] + ρ*di[:mHN]*di[:νHN]\n",
    "    end\n",
    "    diX[:welfare] = yh*diX[:mHO] + yd*diX[:mDO] + yl*(diB[:mLO]+diS[:mLO])\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "plannerTechChoice (generic function with 1 method)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function plannerTechChoice(diPara)\n",
    "    ρB, nB, qB = diPara[:searchB]; ρS, nS, qS = diPara[:searchS]; yh, yd, yl = diPara[:values]\n",
    "    λu, λd, r, mD = [diPara[vrb] for vrb in [:λu,:λd,:r,:mD,]]\n",
    "    ## loop over all possible θs\n",
    "    plannerWelfare = -Inf; plannerθLO = NaN; plannerθHN = NaN\n",
    "    for θLO in [0.0,1.0]\n",
    "        for θHN in [0.0,1.0]\n",
    "            cvgDemog, diB, diS, diX = solveDemographics(diPara,θLO,θHN,absorbingBounds=true)\n",
    "            cvgValue = solveValueFunctions!(diPara,diB,diS,diX,θLO,θHN,absorbingBounds=true)\n",
    "            marketQuality!(diPara,diB,diS,diX,θLO,θHN)\n",
    "            if plannerWelfare < diX[:welfare]\n",
    "                plannerWelfare = diX[:welfare]\n",
    "                plannerθLO = θLO; plannerθHN = θHN\n",
    "            end\n",
    "        end\n",
    "    end\n",
    "    ## return\n",
    "    return plannerθLO, plannerθHN, plannerWelfare\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "solveEquilibrium (generic function with 1 method)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function solveEquilibrium(diPara;guess=rand(2))\n",
    "    function resolveX(x)\n",
    "        θLO = 1.0 - x[1]; θHN = 1.0 - x[2] # need to be flipped\n",
    "        cvgDemog, diB, diS, diX = solveDemographics(diPara,θLO,θHN,absorbingBounds=true)\n",
    "        cvgValue = solveValueFunctions!(diPara,diB,diS,diX,θLO,θHN,absorbingBounds=true)\n",
    "        return θLO, θHN, diB, diS, diX\n",
    "    end\n",
    "    function f!(fvec,x)\n",
    "        θLO, θHN, diB, diS, diX = resolveX(x)\n",
    "        fvec[1] = diS[:vLO] - diB[:vLO]\n",
    "        fvec[2] = diS[:vHN] - diB[:vHN]\n",
    "#         fvec[1] = diS[:ζLO] - diB[:ζLO]\n",
    "#         fvec[2] = diS[:ζHN] - diB[:ζHN]\n",
    "    end\n",
    "    lb = zeros(2); ub = ones(2)\n",
    "    sol = mcpsolve(f!, lb, ub, 1.0 .- guess, reformulation=:smooth)#, autoscale=false)\n",
    "    converging = sol.x_converged | sol.f_converged\n",
    "    θLO, θHN, diB, diS, diX = resolveX(sol.zero)\n",
    "    \n",
    "    marketQuality!(diPara,diB,diS,diX,θLO,θHN)\n",
    "    return converging, θLO, θHN, diB, diS, diX\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 4: Equilibrium technology choice plotted against asset supply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 1140x480 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject Text(0.5, 35.76375558614831, '\\\\shortstack[l]{Asset supply, $s$}')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## range of asset supply, s\n",
    "mD = 0.1 # dealer size\n",
    "xRange = range(1e-3,1.0+mD-1e-3,length=1000)\n",
    "\n",
    "## initialize output\n",
    "di = Dict()\n",
    "\n",
    "## compute\n",
    "guess=[0.99,0.01,] # set some initial guess\n",
    "for x in xRange\n",
    "    ## update parameters\n",
    "    diPara = Dict(\n",
    "        :s  => x,:mD => 0.1, # s is being changed\n",
    "        :λu => 0.1,:λd => 0.1,:r=>0.05,\n",
    "        :values => (1.0, 0.9, 0.0), # yh, yd, yl\n",
    "        :searchB => (3.0, 1, 0.5), # ρB, nB, qB\n",
    "        :searchS => (3.0, 5, 0.0), # ρS, nS, qS\n",
    "    )\n",
    "    converging,θLO,θHN,diB,diS,diX = solveEquilibrium(diPara,guess=guess)\n",
    "    di[x] = Dict(\n",
    "        :converging=>converging,\n",
    "        :θLO=>round(θLO,digits=5),:θHN=>round(θHN,digits=5),\n",
    "        :diB=>diB,:diS=>diS,:diX=>diX\n",
    "    )\n",
    "    guess = converging ? [θLO,θHN,] : rand(2) # recursively update guess based on the previous eqm\n",
    "end\n",
    "\n",
    "## find thresholds for s\n",
    "s1  = xRange[findlast([di[x][:θHN] for x in xRange] .== 0.0)]\n",
    "s2  = xRange[findfirst([di[x][:θHN] for x in xRange] .== 1.0)]\n",
    "s2p = xRange[findlast([di[x][:θLO] for x in xRange] .== 1.0)]\n",
    "s1p = xRange[findfirst([di[x][:θLO] for x in xRange] .== 0.0)]\n",
    "\n",
    "## set up canvas\n",
    "fig = PyPlot.figure(figsize=(9.5,4), facecolor=\"w\", dpi=120) # create figure\n",
    "fig.subplots_adjust(left=.1, right=.9, bottom=0.17, top=.93) # reduce white spaces\n",
    "ax1 = fig.add_subplot(111) # create axis for total trading cost\n",
    "ax2 = fig.add_subplot(111, sharex=ax1) # create axis for attention\n",
    "\n",
    "## plot thetas\n",
    "ax1.plot(xRange, [di[x][:converging] ? di[x][:θHN] : NaN for x in xRange], ls=\"-\", c=\"b\")\n",
    "ax1.plot(xRange, [di[x][:converging] ? di[x][:θLO] : NaN for x in xRange], ls=\"--\", c=\"r\")\n",
    "## plot m_{do}\n",
    "ax2.plot(xRange, [di[x][:converging] ? di[x][:diX][:mDO] : NaN for x in xRange], ls=\"-.\", c=\"g\")\n",
    "\n",
    "## vertical lines\n",
    "ax1.vlines([s1,s2,s2p,s1p],ymin=0.0,ymax=1.0,linestyle=\":\",lw=0.8,colors=\"gray\")\n",
    "\n",
    "## set axes ticks and ranges\n",
    "for axx in [ax1,ax2,]\n",
    "    xmin, xmax = 0.0, 1.0 + mD\n",
    "    axx.set_xlim([xmin, xmax])\n",
    "    axx.set_facecolor(\"None\")\n",
    "end\n",
    "ax2.set_xticks([])\n",
    "ax1.set_ylim([-0.005,1.1])\n",
    "ax1.set_yticks([0,1])\n",
    "ax2.set_ylim([-0.0005,0.11])\n",
    "ax2.set_yticks([0,0.1])\n",
    "ax2.set_yticklabels([\"0\",L\"$m_d$\"])\n",
    "ax1.set_xticks([0.0,s1,s2,(1.0+mD)/2,s2p,s1p,1.0+mD])\n",
    "ax1.set_xticklabels([\"0\",L\"$s_{hn,0}$\",L\"$s_{hn,1}$\",\"\",L\"$s_{lo,1}$\",L\"$s_{lo,0}$\",L\"$1+m_d$\"])\n",
    "ax1.annotate(L\"$\\eta+\\frac{m_d}{2}$\",xy=(0.465,0.02),xycoords=\"axes fraction\")\n",
    "\n",
    "## format axes\n",
    "ax1.spines[\"right\"].set_visible(false)\n",
    "ax1.spines[\"top\"].set_visible(false)\n",
    "twin_arrowed_spines(ax1, which_spines=[\"left\",]) # add arrows\n",
    "ax1.yaxis.set_ticks_position(\"left\") # only show vertical ticks on left\n",
    "ax1.xaxis.set_ticks_position(\"bottom\") # only show horizontal ticks on bottom\n",
    "ax2.spines[\"left\"].set_visible(false)\n",
    "ax2.spines[\"top\"].set_visible(false)\n",
    "twin_arrowed_spines(ax2, which_spines=[\"right\",]) # add arrows\n",
    "ax2.yaxis.set_ticks_position(\"right\") # only show vertical ticks on left\n",
    "ax2.xaxis.set_ticks_position(\"bottom\") # only show horizontal ticks on bottom\n",
    "\n",
    "## label axis next to arrows\n",
    "ax1.annotate(L\"\\shortstack[l]{Customers' technology choices,\\\\$\\theta_{hn}$ (solid line) and $\\theta_{lo}$ (dashed line)}\", xy=(0.014,0.94,), xycoords=\"axes fraction\") # left y label\n",
    "ax2.annotate(L\"\\shortstack[r]{Dealer-owner size,\\\\$m_{do}$ (dot-dashed line)}\", xy=(.74,0.94,), xycoords=\"axes fraction\") # right y label\n",
    "ax1.set_xlabel(L\"\\shortstack[l]{Asset supply, $s$}\") # x-axis label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 5: Usage of SMS in a stationary equilibrium after surges in supply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZmjRmVlN1bW1tsNvt0/YPAj7YzTK+EWO8jRs3xg4rMJdtiIiIsk1tYUDaMac1Tuk04FJbxqfqcNrc3AwACf0rZrMNERER0Xyb9waYajOzVAvI1REltb3/bLeZqZtuugnLli2b1bZERESkTbNeHJ4On88XWweVqu1+/KiVelyemW4zm8MlLF68GE899RRWrFiBV88PYrRKnvF1UGGoKi+Bqf5y0WUQEVEemPfgpEo1rRe/8Nvn8yX8f7rbpKOnpwe9vb0Jp505cwYA8LUnXkRZferDSFDh+8XX78Tyq6pFl0FERDluXoNT/F536bQsmLinXrrbpGP37t34zne+k9ZlSTvKMYqlJT3Y/P1f45vrbsKWO65HUZFOdFlERJSj5jU4xZuqdYF6NO6Jl5nNNqls3bp10oFwz5w5g7vvvhv/cPcKLLphaVrXQ4VjbFyB49BJ6HrOIxIZw/86cAq/ea0X/59tJeoNetHlERFRDprX4BQ/YjTVEdXV8CNJ0qy2SUdDQwMaGhqSnne7qRbLlyY/jwqbZdkV8Lx5K76x70W8HbiEX7/Wi3X/+zf4rnUlPsHXBBERTTCve9WtWrUq9u90RoYaGxtntQ3RbI2MjKBeF8R/3H877r7lKgBA38URbPm3o/j2z04iNDomuEIiIsol8xqc4keQUo0exS/utlgss9qGaLZkWcbevXsxFhrC/2/zrfinTStxuT46EPtvv3sDd3//MF67EBRcJRER5Yp57+OkHviwu7s76flqCJIkKdZWYDbbEM1GbW0ttm3bhtraWgDAhluvwTNf/yhuuVYCALz6XhCf/efn8O9H3oSiKAIrJSKiXDDvwam1tRXAB00tJ1KP8bN9+/Y5bUM0G8XFxTAYDCguLo6dtrC2As6vfARf++Ri6HRAODKOv3n6BL764xcxGBoVWC0REYk278HJbDbDYrHA6/Um7bnkcDggSRJaWlrmtA3RbMiyjCeeeGLSerrS4iI88Kkl2HffaiyoLgcA/OLl8/jsPz+HE+8MCKiUiIhywZyC09GjRwFMv4jb6XTCbDbDZrMlXFYdWTp06NCkveNmsw3RTBUVFaG8vBxFRcnfCrebavHM1z+KTyypBwC82T+Mz+3+HafuiIg0SqfM4tPf5XLh6NGj6OjoiJ1msVjQ3NwMq9Wact1RR0cHurq6Yj2YTCYT7Hb7lAFoNtuk6+TJk1ixYgVOnDiB5cuXz/n6qHCNjytw/NaH7/6/0xgbj75l1t+8ADs/dxMM5aWCqyMiomyZVXAqFAxOFIlE4Pf7YTQaUVIyfVuzo2/48bUfv4j3BkMAgEV1lfje52/l4VqIiDRi3tc4EeUyv9+PPXv2TNlsNd5t1xvxi6/fiY9/KDp1d7ZvCBt2/w4//v05Tt0REWkAgxNpmtFoxP3335/ygNLJ1F6ux94v34a/+vQSFOmAkcg4Htz/Mtp/8hIbZhIRFTgGJ9K0kpISNDQ0pDVNF6+oSIe//MRiPHHf6thx7Z469jY2dj6Pd+RL81EqERHlAAYn0rTBwUHs378fg4ODs9r+dlMtfvG1O7HquhoAwEtvD+Cz//wcDp/py2SZRESUIxicSNPGx8cRCoUwPj4+6+toqCrHj+9bjS995DoAgH9oBH/2g9+j89fdXPdERFRguFcd96qjDPqJ5208uP9lhCPRIPZHNy2A3Xpz7Ph3RESU3zjiRJo2NjaGYDCIsbHMLOr+k6Zr8JP778A1NZcBiHYb3/D9w/D1XszI9RMRkVgMTqRp/f392LVrF/r7+zN2nSuursbPv3onPnpDHQDg9Z6L+O/fP4zfvNabsdsgIiIxGJxI0yRJwpYtWzJ++J6ayjL825YP4y8/0QgACIYi+PLeF/DYc2e57omIKI8xOJGmlZWVYeHChSgrK8v4dRcX6fBXn16K733+VpSXFmFcAf7+wCls/+nLGInMfjE6ERGJw+BEmhYMBnHw4EEEg8F5u431N18FZ+sduLKqHACw7+hb+NN//T36L4bn7TaJiGh+MDiRpkUiEZw/fx6RSGReb+ema6rxs6+uwS3XSgCAF97w44+/dxivnJ9d/ygiIhKDwYk0raamBvfeey9qamrm/bYaqsqxr2U1Pnfr1QCAd+RL+JM9v8MvT74377dNRESZweBEmqYoCkZHR7O2YLu8tBiPbFyJ/7luKXQ6YHhkDK3/14N//a2Pi8aJiPIAgxNpWm9vLx5++GH09mavVYBOp8NXPt6If/3iKlSWFUNRgH/4xSv4u5+dRGSMi8aJiHIZgxNpWnV1NTZv3ozq6uqs3/baG6+A8ysfLBr/4fNvouXfPRgKz+96KyIimj0GJ9I0vV6PJUuWQK/XC7n9ZVdVYf9f3oEbF1QBAP7r1R5s7HweFwZDQuohIqKpMTiRpg0NDeHZZ5/F0NCQsBoWVF8G51c+go9/qB4AcPLdQdz9/cN49T3ucUdElGsYnEjTwuEwTp06hXBYbE+ly/Ul+MGXVuELty8EAJwfCMG653n8modpISLKKQxOpGlGoxFbt26F0WgUXQpKiovwD3evwPZ1SwEAF8MR3PtvR+HyvC24MiIiUjE4EeUQnU6H1o83YvcXzNCXFGFsXMH/cB7Hv/y6m+0KiIhyAIMTaVpvby8eeuihrLYjSMdnblqAf//z21FVXgIA+MeDr+IffvEKxscZnoiIRGJwIk0zGAxYv349DAaD6FIm+fAiI5xfuQNXVEX3+PvBc2fxraf+wAMEExEJxOBEmlZeXo6VK1eivLxcdClJLbnSgJ/cfwdM9ZUAgP/4w7v488ePstcTEZEgDE6kacPDwzhy5AiGh4dFl5LSNTUVcH3ljtgBgn/7eh8+/+gR9F8UuycgEZEWMTiRpg0PD+Pw4cM5HZwAwFhZhh/fdzvuWhLt9XT87QFY/+V5vOXP7bqJiAoNgxNpWl1dHR544AHU1dWJLmVaFWUlePSLq/C5W68GAJztG8LGzufh670ouDIiIu1gcCLKI6XFRfj/bCvxF3cuAhBtlLmx83m8cp5dxomIsoHBiTStr68PjzzyCPr6+kSXkraiIh12/NGN+KblBgBA38URbHYcwR/eksUWRkSkAQxOpGkVFRVYs2YNKioqRJcyIzqdDt+0fAh//Uc3AgAGLo3iC48ewRFfv+DKiIgKG4MTaVpFRQVWr16dd8FJ9RcfNeHhDTdBpwOGRsbwpcdewLOne0SXRURUsBicSNNCoRCOHz+OUCgkupRZ+/ztC/FPG29BcZEO4cg47vvhMRx8+bzosoiIChKDE2laMBjEgQMHEAwGRZcyJ3ffejV2f8GMsuIijI4p+Msfe7H/RR4cmIgo0xicSNPq6+uxY8cO1NfXiy5lzj69/Er84MurUF5ahHEF2PbUcfzEw/BERJRJDE5EBeSjN9Tjh/fejoqyYigK8D9cx+FieCIiyhgGJ9I0v9+P3bt3w+/3iy4lYz68yIjH7/0wKt8PT3/lOg7nsbdEl0VEVBAYnEjT9Ho9li1bBr1eL7qUjLrt+sTw1PaTl/AUwxMR0ZwxOJGmVVZW4q677kJlZaXoUjJu1YTw1P6Tl/DUUYYnIqK5YHAiTQuHwzh9+jTC4bDoUubFquuN+OGffxiX60tiI09PHj0nuiwiorzF4ESaNjAwgH379mFgYEB0KfOm6broyNPl+hIAQPtPXuaCcSKiWWJwIk2rr6/Hgw8+WBDtCKbSdF1NbOQJANpcx3HgpXcFV0VElH8YnEjTdDodSktLodPpRJcy78wLa7B3y224rLQY4wrwzX1/QNepC6LLIiLKKwxOpGmBQACPPfYYAoGA6FKy4rbrjfjXL61CWUkRIuMK/vJHXvzmtV7RZRER5Q0GJ9K0kpISLFiwACUlJaJLyZo1i+vQ+adNKC3WYWRsHC3/fgy/9/WLLouIKC8wOJGmGQwGrFu3DgaDQXQpWfWJpQ34P5tvRXGRDqHRcdz7b0fx4jltjLoREc0FgxNp2sjICM6dO4eRkRHRpWTdupsW4BHbSuh0wNDIGL702As48U7h7l1IRJQJDE6kabIsY+/evZBlWXQpQtx969X4x8/dBAAYDEXwpcdewNm+IcFVERHlLgYn0rTa2lps27YNtbW1oksRZtNtC/Htzy4DAPQPjeDPfvB7XBgMCa6KiCg3MTiRphUXF8NgMKC4uFh0KUJ9ec0ifH3tDQCAtwOX8MUfvICB4VHBVRER5R4GJ9I0WZbxxBNPaHaqLt63LDfgz1ZfBwA4fSGIex8/iksjY4KrIiLKLQxOpGlFRUUoLy9HURHfCjqdDt/+4+X4o5sXAAA8bwaw9UcejI6NC66MiCh38NuCNK2qqgobNmxAVVWV6FJyQnGRDrs2rsSdi+sAAL863Ys210sYH1cEV0ZElBsYnEjTIpEIenp6EIlERJeSM/Qlxej8syasvKYaALD/xXfwD794BYrC8ERExOBEmub3+7Fnzx74/X7RpeSUSn0J9m75MEz1lQCAxw6fheM3PsFVERGJx+BEmmY0GnH//ffDaDSKLiXnGCvL8O9/fjsWVJcDAHYefBUHXnpXcFVERGIxOJGmlZSUoKGhQVPHqpuJq6XLsHfLbTDoo4/PtieP44WzHJ0jIu1icCJNGxwcxP79+zE4OCi6lJy19Moq/MufNaGkKHpQ4Pt+eAzdvRdFl0VEJASDE2na+Pg4QqEQxse5y/1U1iyuwz/+yc0AgIFLo/jy3hfQGwwLroqIKPsYnEjTJEnCPffcA0mSRJeS86xN1+Bblg8BAN7yX8JfPH4UwyPcG5GItIXBiTRtbGwMwWAQY2PskJ2Or69dDGvTNQCA428P4OtP/AFj7PFERBrC4ESa1t/fj127dqG/v190KXlBp9Nh5+duwkdviDbIdL9yAX//85OCqyIiyh4GJ9I0SZKwZcsWTtXNQGlxEXZ/wYylVxoAAI8//yZ++PwbYosiIsoSBifStLKyMixcuBBlZWWiS8krhvJS7N1yGxoMegDAd35+Cr99vVdwVURE84/BiTQtGAzi4MGDCAaDokvJOwuqL8OjX1wFfUkRxsYVbP2Rl20KiKjgMTiRpkUiEZw/f57HqpullddK+K5tJQAgGIrgLx4/Bnl4RHBVRETzh8GJNK2mpgb33nsvampqRJeSt/545VX4+tobAABn+4bwlz/2YnSMfbGIqDAxOJGmKYqC0dFRKAp3qZ+Lb669AZ+56UoAwOEz/fgO97QjogLF4ESa1tvbi4cffhi9vVzYPBdFRTo8YrsFN11dDQD4v0fO4fHfvSG2KCKiecDgRJpWXV2NzZs3o7q6WnQpee+ysmI8+sVVsT3t/v7AKTzfzf5YRFRYGJxI0/R6PZYsWQK9Xi+6lIJwZXU5Hv3iKpS9v6fdV3/sxTvyJdFlERFlDIMTadrQ0BCeffZZDA0NiS6lYKy8VsJDd68AAPQPjeAr/+5BaJSHtCGiwsDgRJoWDodx6tQphMNh0aUUFNuqa/Glj1wHAHj5nQHs2H+CC/CJqCBkPTjJsozW1lY0NzejsbERjY2N6OjomHa7jo4ONDc3o7m5GU1NTWhtbYUsy/NfMBU0o9GIrVu3wmg0ii6l4Pz1+mX48PXRx/Un3rfx70feFFwREdHcZTU4ud1uLFq0CE1NTejq6kJ3dzecTifa29vR3NyccrumpiZ0dnbC6XSiq6sLHo8Hfr8fixYtgtfrzeI9IKJ0lRYX4ftfMOPKqnIAwN///BReOOsXXBUR0dxkLTjJsgybzQaTyYSWlpbY6WazGU6nE263GzabbdJ2NpsNXq8XTqcz4UCsTqcTALB27dp5r50KV29vLx566CG2I5gn9QY99vypGWXFRYiMK9j6Iw/OD3CxOBHlr6wFp/b2dsiyjE2bNk06z2q1AgBcLhd8Pl/sdK/XC5fLBZPJBLPZPGm7jRs3QpZlOByO+SucCprBYMD69ethMBhEl1Kwbl1Yg/9193IAQN/FEXzl/3oxEmFncSLKT1kLTk899RQAwGQyJT3fYrEAiAYs1c6dOxPOm0id3rPb7Rmrk7SlvLwcK1euRHl5uehSCtqm2xbiC7cvBAAcf0vGw8+8IrgiIqLZyUpwkmU5tpA7frotnjqiFL9mye12A4iucZpqG5/PlzBSRZSu4eFhHDlyBMPDw6JLKXh/+9llWHlNtNHov/3uDfzipfOCKyIimrmsBCe//4MFoan2hKutrQWAWADy+Xyxy6YapYrfE4qLxGk2hoeHcfjwYQanLNCXFOP7XzCj+rJSAED7T16Cr/ei4KqIiGYmK8EpPuCkGhmKH4maOIKUalfxidsQzVRdXR0eeOAB1NXViS5FE66pqcCujSsBABfDEWz9kZfNMYkor2QlOEmSFAs5Tz755LSXNxqNCSNTqab34vX3T31MrJ6eHpw8eTLh78yZM9NeLxFl1tobr8BXPt4IAHj1vSD+9j9OCK6IiCh9WVscvn37dgDRKbVk02oejyf274lBaapGl+plp2uGuXv3bqxYsSLh7+67706ndCpgfX19eOSRR9DX1ye6FE35H5/6ED68KDqS/NSxt+E89pbgioiI0pO14NTW1hZbzN3U1ASHwxFrN9Dc3BxbCK6uZ4oPT/FrpCaabtG5auvWrThx4kTC39NPPz3r+0OFoaKiAmvWrEFFRYXoUjSlpLgI37vnVtRdXgYA+Jv/OIFX3xsUXBUR0fRKsnljHo8HHR0dePLJJ9Ha2hrrz9TZ2RlrLdDa2goAWLVqVWy7dA6t0tjYOOX5DQ0NaGhomH3xVJAqKiqwevVq0WVoUkNVOf7P5lvxpz/4PUKj49j6f734+dfuRKU+qx9LREQzkvVj1bW1tcHj8UBRlNghV2RZji3uVpthxq+LSjXiFL8gPFWvJ6KphEIhHD9+HKFQSHQpmnTH4jp8y/IhAICvbwjf/tlJwRUREU0t68EpGbXRZUtLS0LrgY0bNwIAuru7k26nBidJklK2LCCaSjAYxIEDBxAMBkWXollbP7EYdzRG25E4PW/jZ8ffFVwREVFqwoOTw+GAy+WCJEmTOoCr03bq+qeJurq6AHyw8Jxopurr67Fjxw7U19eLLkWziot0+KdNt6CmItrfacdPX8ZbfvbVIqLcJDQ4eb1etLa2QpIkHDp0aNICb7PZDIvFAq/Xm7RPk8PhgCRJCQcNJqL8c0VVOb5rjfZ3CoYj+Pq+FzE6xuPZEVHuERacHA4HmpqaYDKZ4PF4kh7EFwCcTifMZjNsNlvCInF1NCpZ4CJKl9/vx+7du6fcc5Oyw7LsCnz5jusBAC+ek/G/3a+LLYiIKImsBye3243m5ma0traira0N3d3dU65PkiQJHo8HmzZtgs1mg81mi+2Bd/bs2ZSBiygder0ey5Ytg16vF10KAfif65Zi6ZUGAMD3nz2D33WzvxYR5RadoihKNm7I7XbDbrfDZDKhubk5tvecSCdPnsSKFStw4sQJLF++XHQ5RATg9QtBfPZ7zyE0Oo4rqvQ4+I2PwVhZJrosIiIAWQxOuYjBicLhMN544w1cf/31HHXKIU+8cA7bf/oyAMBy4xV49ItN0Ol0gqsiIsqBveqIRBoYGMC+ffswMDAguhSKs/m2a7FuxZUAAPcrF/DkUR6ShYhyA4MTaVp9fT0efPBBtiPIMTqdDv/4uZtxRVV0FPB/HTiFc/1sUUBE4jE4kabpdDqUlpZyGigHVVeUxloUDI2M4QHnHzA2rtmVBUSUIxicSNMCgQAee+wxBAIB0aVQEh/7UD2++JHrAABH3wjg0d9O7udGRJRNDE6kaSUlJViwYAFKSnhg2Vy1fd2NMNVVAgB2/fI1vHJ+UHBFRKRlDE6kaQaDAevWrYPBYBBdCqVwWVkxHtm4EsVFOoyMjeNbT/4B4ciY6LKISKMYnEjTRkZGcO7cOYyMjIguhaZw68Ia/OVdjQCAV98L4p+62FWciMRgcCJNk2UZe/fuTTicD+Wmr629ATddXQ0A6PxNN46+wcPkEFH2MTiRptXW1mLbtm2ora0VXQpNo7S4CP+0aSX0JUVQFGDbU3/AUDgiuiwi0hgGJ9K04uJiGAwGFBcXiy6F0rC4wYD2/7YUAPCW/xK++/9OC66IiLSGwYk0TZZlPPHEE5yqyyNfvuN63HZ9DQDg8eff4JQdEWUVgxNpWlFREcrLy1FUxLdCvigq0qHD+sGUXZvrJVwa4V52RJQd/LYgTauqqsKGDRtQVVUluhSagUV1lfgfn1oCADjbN4R/cr8muCIi0goGJ9K0SCSCnp4eRCJcZJxv7r1zEW65VgIA/OtvfXjxHLu/E9H8Y3AiTfP7/dizZw/8fq6TyTfFRTp813ozyoqLMK4Af+V6CaFRTtkR0fxicCJNMxqNuP/++2E0GkWXQrNwwxUGfMNyAwDgTM9F/J9DbIxJRPOLwYk0raSkBA0NDTxWXR5r+ZgJK66OrlHr/I0PJ94ZEFwRERUyBifStMHBQezfvx+DgzxwbL4qLS7Cd60rUVKkw9i4gv/505cQGRsXXRYRFSgGJ9K08fFxhEIhjI/zizaf3bigCl/5ePRYdifeGcS//e4NsQURUcFicCJNkyQJ99xzDyRJEl0KzdFXP7kY19dWAAB2db2Gd+RLgisiokLE4ESaNjY2hmAwiLEx7o2V78pLi/HQhpsAAMMjY/jbp09AURTBVRFRoWFwIk3r7+/Hrl270N/fL7oUyoA1i+vwOfPVAIBDr/bg4In3BFdERIWGwYk0TZIkbNmyhVN1BeSv/2gZaipKAQDf/tlJDIZGBVdERIWEwYk0raysDAsXLkRZWZnoUihDjJVl2PFHywAAPcEwOv7zVcEVEVEhYXAiTQsGgzh48CCCwaDoUiiD/sR8Ne5orAUA/Oj35+B5k4djIaLMYHAiTYtEIjh//jyPVVdgdDodHtpwE8pKiqAowI79L7O3ExFlBIMTaVpNTQ3uvfde1NTUiC6FMmxRXSW+9onFAIBX3wvih8+/KbgiIioEDE6kaYqiYHR0lLutF6iWj5tivZ3+qes19ARDgisionzH4ESa1tvbi4cffhi9vb2iS6F5oC8pxrf/eDkAIBiO4B+f4UJxIpobBifStOrqamzevBnV1dWiS6F5cteSBnxq2RUAgJ+++A5+72PPLiKaPQYn0jS9Xo8lS5ZAr9eLLoXm0d+sX4by0ujH3d/97CQXihPRrDE4kaYNDQ3h2WefxdDQkOhSaB5da6zAV7lQnIgygMGJNC0cDuPUqVMIh8OiS6F5dt/HuFCciOaOwYk0zWg0YuvWrTAajaJLoXnGheJElAkMTkSkGRMXih97wy+4IiLKNwxOpGm9vb146KGH2I5AQ/5m/TLoS6IffX9/4BTGx9nDi4jSx+BEmmYwGLB+/XoYDAbRpVCWXGusQMvHTACAl94ewP4X3xFcERHlEwYn0rTy8nKsXLkS5eXlokuhLPrKxxvRYIi2oLD/56sYCvNYhUSUHgYn0rTh4WEcOXIEw8PDokuhLKrUl6D9vy0FAPQEw/iXX3cLroiI8gWDE2na8PAwDh8+zOCkQRtuvRo3XxPtGO/4jQ9vB/gaIKLpMTiRptXV1eGBBx5AXV2d6FIoy4qKdPjb9csAAOHIOP7xINsTENH0GJyISLNWXW/EZ1deBQA48NJ5ticgomkxOJGm9fX14ZFHHkFfX5/oUkiQ9v+2hO0JiChtDE6kaRUVFVizZg0qKipEl0KCXFOT2J7gp2xPQERTYHAiTauoqMDq1asZnDTuKx9vxBVV0fYEj/zyNEKjY4IrIqJcxeBEmhYKhXD8+HGEQjzgq5ZV6kuwrflDAIDzAyHsPfyG2IKIKGcxOJGmBYNBHDhwAMFgUHQpJNifmK/BDQ2XAwB2P3sGgaERwRURUS5icCJNq6+vx44dO1BfXy+6FBKspLgI/3NdtClmMBTB9391RnBFRJSLGJyIiN73yaUN+PAiIwDgh8+/ibf8bIpJRIkYnEjT/H4/du/eDb+f/XsI0Ol02P7+qNPI2Dge+eVpwRURUa5hcCJN0+v1WLZsGfR6vehSKEfcurAGf3TTAgDA0394FyfeGRBcERHlEgYn0rTKykrcddddqKysFF0K5ZC/+vQSlBTpAICHYiGiBAxOpGnhcBinT59GOBwWXQrlkOvrKvH52xcCAJ4704ffvNYruCIiyhUMTqRpAwMD2LdvHwYGOB1Dib6+9gZUlhUDAHYefJWHYiEiAAxOpHH19fV48MEH2Y6AJqm7XI+WjzUCAF45P4hnTpwXXBER5QIGJ9I0nU6H0tJS6HQ60aVQDvrzjy6CsbIMALCr6zVExsYFV0REojE4kaYFAgE89thjCAQCokuhHHS5vgT3fzw66uTrHcLTf3hXcEVEJBqDE2laSUkJFixYgJKSEtGlUI76s49chwZDtF3F/z70GkYiHHUi0jIGJ9I0g8GAdevWwWAwiC6FclR5aTG+9snFAIC3/Jfw1LG3BFdERCIxOJGmjYyM4Ny5cxgZ4QFdKbVNty3E1dJlAIB//q/XERodE1wREYnC4ESaJssy9u7dC1mWRZdCOayspAjfsNwAALgwGMaPfn9OcEVEJAqDE2labW0ttm3bhtraWtGlUI773K1Xw1QX7TC/+1dnMBSOCK6IiERgcCJNKy4uhsFgQHFxsehSKMeVFBfhm80fAgD0D43g3373htiCiEgIBifSNFmW8cQTT3CqjtKy/qYFWHpldEeCzl93Y+DSqOCKiCjbGJxI04qKilBeXo6iIr4VaHpFRTpse3/UaTAUwWPPnRVcERFlG78tSNOqqqqwYcMGVFVViS6F8kTzsitw09XVAIC9h89iMMRRJyItYXAiTYtEIujp6UEkwoW+lB6dToevr43uYTcYiuCHXOtEpCkMTqRpfr8fe/bsgd/vF10K5RHLjQ24cUF0lPJfnzuLi9zDjkgzGJxI04xGI+6//34YjUbRpVAe0el0+MbaaDdxeXgU//78m4IrIqJsYXAiTSspKUFDQwOPVUcz9qllV2LJFdE97B79rQ/DIxx1ItICBifStMHBQezfvx+Dg4OiS6E8U1Skw9feH3XyD43gR0fYTZxICxicSNPGx8cRCoUwPs4j3tPMrVuxAIsbLgcAdP7Gh0sjPIYdUaFjcCJNkyQJ99xzDyRJEl0K5aHiIh2+9snoqFPfxTB+/AJHnYgKHYMTadrY2BiCwSDGxjhSQLOz/uarYsew+5dfdyM0ytcSUSHLanCSZRmtra1obm5Gc3MzmpqaYLPZ4PV6p9yuo6MjYZvW1lYeIoMyor+/H7t27UJ/f7/oUihPFRfp8JefiI469QbDcHneFlwREc2nrAUnr9eLmpoaNDY2oqurC11dXfB4PDCZTGhqaoLL5Uq6XVNTEzo7O+F0OmPb+P1+LFq0aNrARTQdSZKwZcsWTtXRnPzxLVfhaukyAIDjNz5ExrhmjqhQ6RRFUbJxQzU1NTCZTPB4PJPOa2xshM/nw8RSbDYbXC4XPB4PzGbzpOsDgEAgMOuaTp48iRUrVuDEiRNYvnz5rK+HiOiHz7+Bv/2PkwCA/3PPrfjjlVcJroiI5kNWRpy8Xi9kWYbJZEp6vhqKfD5fwjYulwsmk2lSaAKAjRs3QpZlOByO+SmaNCEYDOLgwYMIBoOiS6E8Z2u6FrWVZQCAPc92T/ohSESFISvBSQ1EqabWkq1X2rlzJwDAYrEk3aa5uRkAYLfbM1AhaVUkEsH58+d5rDqas8vKirFlzfUAgFfOD+LZ13rFFkRE8yIrwSl+RKm9vT3hPFmW4Xa7YbFYEkak3G43gOgap+muM36kimgmampqcO+998amfonm4s9WX4/KsmIA0VEnIio8WQlOJpMJLS0tAKJ7yKnhSZZlrF27FmazGU6nM3Z5n88XG4VKNb0Xf2wxLhKn2VIUBaOjo5xWoYyorijFn66+DgDwwlk/PG/y4NFEhSZre9V1dnbCarUCiIanpqYmNDU1YdOmTfB4PAl7NcWPIKU6+Gqqy6fS09ODkydPJvydOXNmdneGCkZvby8efvhh9PZyWoUy4947F6GsOPrRuudZjoYTFZqs9nFyOp2xkSev1wufz5d0N/D4NU/p7CaeTg+e3bt3Y8WKFQl/d999d5qVU6Gqrq7G5s2bUV1dLboUKhBXVJXjT5quBgC4X7mA0+9xxwOiQpL1zuF+vz9hwXdraytaW1tTXn6qRpdqqEqnGebWrVtx4sSJhL+nn346zaqpUOn1eixZsgR6vV50KVRAWj/WiCJd9N+dv+ZaJ6JCkrXgJMsyGhsbYTQa0dXVhe7u7tj6JYfDkRCe4keZ/P7UawTUwJTOqFRDQwOWL1+e8Ld48eJZ3RcqHENDQ3j22WcxNDQkuhQqINfXVWLdTQsAAP9x/F28K18SXBERZUrWgtPatWvh9/vR2dkJILrou7u7O7buyeFwxPakW7VqVWy7dEaTGhsbM18waUI4HMapU6cQDodFl0IF5isfi34ujY0rePx3b4gthogyJivByeVywev1YuPGjZPOczqdsdYCaqiSJCk2ipRqxCl+QXiqXk9E0zEajdi6dWvKnRCIZuuma6rx4UXR19WPXziHi2H2CiMqBFltgJlqSm379u0JlwMQC1nd3cnXB8RfZ6qWBUREIt330ehnUzAUwVNH3xJcDRFlQtb6OAGp2wao58dP0alrntTpu4m6uroAfBC6iGajt7cXDz30ENsR0LxYu7QBi+oqAQCPHT7Lg/8SFYCsBCd1Ks3tdidds6SGI/UwKkC0M7jFYom1LZjI4XBAkqRYewOi2TAYDFi/fj0MBoPoUqgAFRXpcO+diwAAbwcu4ZenLgiuiIjmKivBSZIkOJ1OyLIMm82WEJ68Xi927tyJlpaW2EJxlbr+aeI26mjUoUOH0tqjjiiV8vJyrFy5EuXl5aJLoQJlNV8DqaIUAPDob9kQkyjflWTrhqxWK7q7u9He3o6mpiZIkhRbkOt0OpMu8JYkCR6PBx0dHbDZbJAkCbIsw2Qy4ezZswxNNGfDw8N46aWXcPPNN6OiokJ0OVSALisrxp/efh2+96szePGcDM+bATRdx2MjEuUrnaLhg3SdPHkSK1aswIkTJ7B8+XLR5ZAAfX19ePzxx/GlL30JdXV1osuhAtUTDOHOf/wVRsbGsW7Fldjzp8kPXk5EuS/rncOJckldXR0eeOABhiaaVw2GcvzxLVcBAP7fyfdwrn9YcEVENFsMTkREWfAXH40uEh9XonvYEVF+YnAiTevr68MjjzyCvr4+0aVQgVt6ZRU+ekN0ZNN57C0EQ6OCKyKi2WBwIk2rqKjAmjVruDCcsmLLmusBAEMjY/iJ522xxRDRrDA4kaZVVFRg9erVDE6UFXd9qAELjdHX2g+ffxPj45rdN4cobzE4kaaFQiEcP34coVBIdCmkAUVFOnzxI9cBAHx9Q/jtGU4RE+UbBifStGAwiAMHDiAYDIouhTTCtupaXFZaDAB4/HdviC2GiGaMwYk0rb6+Hjt27EB9fb3oUkgjqi8rxQbz1QCAX53uwZv9Q4IrIqKZYHAiIsqyL33kegCAokTXOhFR/mBwIk3z+/3YvXs3/H6/6FJIQ5ZcacBHTLUAgKeOvYWhcERwRUSULgYn0jS9Xo9ly5ZBr9eLLoU05kt3XA8ACIYi2P/iO2KLIaK0MTiRplVWVuKuu+5CZWWl6FJIYyw3NuBq6TIAwA+ffwMaPmwoUV5hcCJNC4fDOH36NMLhsOhSSGNKiovwp6ujrQleu3ARz3f3C66IiNLB4ESaNjAwgH379mFgYEB0KaRBm2+7FvqS6McwF4kT5QcGJ9K0+vp6PPjgg2xHQELUVJZh/c1XAQC6XrmAC4NsxEqU6xicSNN0Oh1KS0uh0+lEl0Ia9YXVCwEAY+MKnjr6luBqiGg6DE6kaYFAAI899hgCgYDoUkijbr1WwtIrDQCAfUffwhiPX0eU0xicSNNKSkqwYMEClJSUiC6FNEqn0+EL7y8Sf0e+hF+/1iO4IiKaCoMTaZrBYMC6detgMBhEl0IadvctV6GiLHr8uh///pzgaohoKgxOpGkjIyM4d+4cRkZGRJdCGmYoL8V/vyW6SPy/Xu3Bu/IlwRURUSoMTqRpsixj7969kGVZdCmkcZ//cHS6blyJrnUiotzE4ESaVltbi23btqG2tlZ0KaRxN11TjZuvqQYAPHn0HCJj44IrIqJkGJxI04qLi2EwGFBcXCy6FCJ84fZoa4ILg2EcepWLxIlyEYMTaZosy3jiiSc4VUc54bMrr4JBH93D80dcJE6UkxicSNOKiopQXl6OoiK+FUi8irISbDBfDQD47eu9ONc/LLgiIpqI3xakaVVVVdiwYQOqqqpEl0IEAPj8+9N1igI8dYyLxIlyDYMTaVokEkFPTw8ikYjoUogAAEuvrMIt10oAAJfnbXYSJ8oxDE6kaX6/H3v27IHf7xddClHMxlXXAgDeGwzht6/3Cq6GiOIxOJGmGY1G3H///TAajaJLIYr57MoFKC+Nfjxzuo4otzA4kaaVlJSgoaGBx6qjnGIoL8VnbloAAOg6dQH+IXa2J8oVDE6kaYODg9i/fz8GBwdFl0KUQJ2uGx1T8PSL7wiuhohUDE6kaePj4wiFQhgfZ5dmyi23LzLiutoKANHpOkXhInGiXMDgRJomSRLuueceSJIkuhSiBDqdLjbq9Op7Qbz8zoDgiogIYHAijRsbG0MwGMTY2JjoUogm+Zz5ahTpov/mInGi3MDgRJrW39+PXbt2ob+/X3QpRJMsqL4MH/tQPQDgP/7wLkKjDPhEojE4kaZJkoQtW7Zwqo5yljpdFwxF8J8n3hNcDRExOJGmlZWVYeHChSgrKxNdClFSlhuvgLEy+vrkdB2ReAxOpGnBYBAHDx5EMBgUXQpRUmUlRbj7luiBf3/X3Y+3/DzwL5FIDE6kaZFIBOfPn+ex6iin2VZdE/v3fvZ0IhKKwYk0raamBvfeey9qampEl0KU0o0LqrD0SgOAaHBiTycicRicSNMURcHo6Ci/iCjn/Yk5Oup0tm8If3hLFlsMkYYxOJGm9fb24uGHH0ZvL49AT7ntv99yVaynE6friMRhcCJNq66uxubNm1FdXS26FKIpNVSVY83iOgDAz4+/i5EIDxNEJAKDE2maXq/HkiVLoNfrRZdCNK3PmaN71wWGR/Hr1zhKSiQCgxNp2tDQEJ599lkMDQ2JLoVoWp9efiUqyooBAPtffFtwNUTaxOBEmhYOh3Hq1CmEw2HRpRBNq6KsBP9t+ZUAAPcrPRgYHhVcEZH2MDiRphmNRmzduhVGo1F0KURp+dz7e9eNRMbxi5fPC66GSHsYnIiI8shHGmtxRVV0TR6n64iyj8GJNK23txcPPfQQ2xFQ3igu0sUOwXL0jQAPwUKUZQxOpGkGgwHr16+HwWAQXQpR2ja8v3cdwJ5ORNnG4ESaVl5ejpUrV6K8vFx0KURpW3plFW5cUAWAh2AhyjYGJ9K04eFhHDlyBMPDnO6g/PK5W6OjTmf7hnDinUHB1RBpB4MTadrw8DAOHz7M4ER5Z/3KBdC9fwiWnx3ndB1RtjA4kabV1dXhgQceQF1dnehSiGZkQfVluO36aBuNAy+dx/g4p+uIsoHBiYgoT3125VUAgPMDIXjOBQRXQ6QNDE6kaX19fXjkkUfQ19cnuhSiGfvMiitRXBSdr/vZH94VXA2RNjA4kaZVVFRgzZo1qKioEF0K0YzVXq7HmsXRaeZnXj6PyNi44IqICh+DE2laRUUFVq9ezeBEeeuzNy8AAPQPjeB33f2CqyEqfAxOpGmhUAjHjx9HKBQSXQrRrHx6xZUoK45+lP/8OKfriOYbgxNpWjAYxIEDBxAMBkWXQjQrVeWluGtJPQDgP0++h3BkTHBFRIWNwYk0rb6+Hjt27EB9fb3oUohm7Y9vie5dFwxF8OvTPO4i0XxicCIiynNrl16BirJiAMDPXzovuBqiwsbgRJrm9/uxe/du+P1+0aUQzdplZcVoXnYFAMB96gKGRyKCKyIqXAxOpGl6vR7Lli2DXq8XXQrRnHz25uh03aXRMfzqVU7XEc0XBifStMrKStx1112orKwUXQrRnHz0Q3W4XF8CAHjmBKfriOYLgxNpWjgcxunTpxEOh0WXQjQn+pJiWG5sAAD86tUeXBrh3nVE84HBiTRtYGAA+/btw8DAgOhSiOZs3U3RZpjDI2P49Ws9gqshKkwMTqRp9fX1ePDBB9mOgArCxz9Uj8r396575uX3BFdDVJgYnEjTdDodSktLodPpRJdCNGflpcX45I3RvesOvXIBoVFO1xFlGoMTaVogEMBjjz2GQCAguhSijPjMiisBAEMjY/jt632CqyEqPAxOpGklJSVYsGABSkpKRJdClBF3LWnAZaXR6bqDL3PvOqJMY3AiTTMYDFi3bh0MBoPoUogy4rKyYnxiaXTNXtcrF3jsOqIMY3AiTRsZGcG5c+cwMjIiuhSijFm3Irp3XTAUweEznK4jyqSsBSdZlqHT6ab9a2xsTLp9R0cHmpub0dzcjKamJrS2tkKW5WyVTwVKlmXs3buXryUqKJ9c2gB9SfTjnXvXEWVW1hZ27Ny5M63LWa3WSac1NTVBlmV4PB5IkgQAsNlsWLRoEQ4dOgSz2ZzJUklDamtrsW3bNlRUVIguhShjKvUluGtJPf7fyQv45cn3MLLhJpSVcIKBKBOy9k5yuVxwOp1QFCXpn9PpBABs2rQpYTubzQav1wun0xkLTQBil1+7dm227gIVoOLiYhgMBhQXF4suhSijPvN+M8zBUAS/6+Z0HVGmZCU4ud1utLe3Jx1NUnV2dkKSpITRI6/XC5fLBZPJlHRUaePGjZBlGQ6HY17qpsInyzKeeOIJTtVRwfnk0gaUFUc/4n956oLgaogKR1aCk8ViQUtLy5SXcbvd2LhxY8Jp6vSexWJJuk1zczMAwG63Z6BK0qKioiKUl5ejqIjTGFRYDOWluGNxLQCg69QFjI8rgisiKgw58W3hcrkARKfl4rndbgDRNU7JqKNQPp8PPp9vHiukQlVVVYUNGzagqqpKdClEGfepZdFmmL3BMP7wtiy2GKICkRPB6cknnwSQOLLk8/li0ycmkynpdkajMfZvr9c7fwVSwYpEIujp6UEkEhFdClHGWZY1QD2a0C9PcrqOKBNyIji5XK5J65/iR5DiA1K8+MXi04049fT04OTJkwl/Z86cmX3RVBD8fj/27NkDv98vuhSijGswlOPWayUAwC9PsS0BUSYIP86EOk03cW+6+MW68QEplf7+/inP3717N77zne/MuD4qbEajEffff3/KcE6U7z61/Ep4z8nw9Q7hTM9FLG64XHRJRHlN+IiTOk031R53U+3xpIaq6faK2rp1K06cOJHw9/TTT8+wWio0JSUlaGho4LHqqGB9atkVsX9z1Ilo7oR/WySbpgMSR5mmmkZRA9N0o1INDQ1oaGiYTYlUwAYHB3Ho0CGsXbuWC8SpIJnqL8fihstxpucifnnyArbetVh0SUR5TeiIk7rX3MRpOgBYtWpV7N/p9NhJdagWoqmMj48jFAphfHxcdClE80YddfrDWzIuDIYEV0OU34QGp87OTgDJ+zRJkhQbRUo14hS/IDxVryeiqUiShHvuuSetdXRE+epTy6+M/buLzTCJ5kRocHK5XLBYLCm/tNSGmN3d3UnPV4OTJEkpWxYQTWVsbAzBYBBjY2OiSyGaNzdfXY0rqvQA2EWcaK6EBSd1mm5i08t4ra2tCZedqKurCwCwffv2DFdHWtHf349du3ZNu1cmUT4rKtKh+f3puue7+zAYGhVcEVH+Ehac1IP0TjzMSjyz2QyLxQKv15u0T5PD4YAkSdMezoUoFUmSsGXLFk7VUcFTu4iPjil49nSv4GqI8pew4PTUU0/BbDZP+4XldDphNpths9kSFomro1GHDh3ilx7NWllZGRYuXIiysjLRpRDNq9WmWhj00R2puc6JaPaEBCe32w1ZlpPuTTeRJEnweDzYtGkTbDYbbDZb7OC+Z8+ejR2vjmg2gsEgDh48iGAwKLoUonlVVlKEjy2pBwD8+nQPImPck5RoNoT0cbJYLFCUmR2pu62tDW1tbfNUEWlVJBLB+fPneaw60oS1Sxvwi5fOYzAUgefNAG431YouiSjvCO8cTiRSTU0N7r33XtTU1IguhWje3bXkg4P+/terPWKLIcpTDE6kaYqiYHR0dMYjoET5yFhZFjvo7yEGJ6JZYXAiTevt7cXDDz+M3l7uZUTasPbGaFuCMz0X8Wb/kOBqiPIPgxNpWnV1NTZv3ozq6mrRpRBlxSeXfnDMTk7XEc0cgxNpml6vx5IlS6DX60WXQpQVS6804KrqcgAMTkSzweBEmjY0NIRnn30WQ0OcsiBt0Ol0+OSN0VGnI75+XAxzj1KimWBwIk0Lh8M4deoUwuGw6FKIsmbt0ug6p9ExBc+9zvV9RDPB4ESaZjQasXXrVhiNRtGlEGXNRxprUV4a/fg/9Aqn64hmgsGJiEhjykuLcefiOgDAr073Ynyc7TiI0sXgRJrW29uLhx56iO0ISHM++f50Xd/FMF5+Z0BwNUT5g8GJNM1gMGD9+vUwGAyiSyHKqvi2BGyGSZQ+BifStPLycqxcuRLl5eWiSyHKqiury7FsQRUA4L9evSC4GqL8weBEmjY8PIwjR45geHhYdClEWfeJpfUAgBPvDKLvIvcsJUoHgxNp2vDwMA4fPszgRJr08Q99MF33W7YlIEoLgxNpWl1dHR544AHU1dWJLoUo625dKMGgLwEA/Po0gxNROhiciIg0qrS4CGveb0vwm9f72JaAKA0MTqRpfX19eOSRR9DX1ye6FCIhPr4kus7JPzSCE++yLQHRdBicSNMqKiqwZs0aVFRUiC6FSIiPf6g+9m9O1xFNj8GJNK2iogKrV69mcCLNukq6DB+64nIAwK9fY3Aimg6DE2laKBTC8ePHEQqFRJdCJIw66uQ9F8DA8KjgaohyG4MTaVowGMSBAwcQDAZFl0IkjNqWYFwBDndzvR/RVBicSNPq6+uxY8cO1NfXT39hogK16voaXFZaDIDrnIimw+BERKRx5aXF+EhjLYDoOidFYVsColQYnEjT/H4/du/eDb/fL7oUIqHUdU7vDYbw2oWLgqshyl0MTqRper0ey5Ytg16vF10KkVAJbQle6xFYCVFuY3AiTausrMRdd92FyspK0aUQCXV9XSWuq4225WBbAqLUGJxI08LhME6fPo1wmEeGJ1JHnV4468fwSERwNUS5icGJNG1gYAD79u3DwAAPNUF05/vHrRsdU3D0jYDgaohyE4MTaVp9fT0efPBBtiMgArC6sRZFuui/n3ud03VEyTA4kabpdDqUlpZCp9OJLoVIuKryUqy8VgIAPHemX2wxRDmKwYk0LRAI4LHHHkMgwGkJIgD46PvTda+cH0TfRa79I5qIwYk0raSkBAsWLEBJSYnoUohywpr3gxMA/K6bo05EEzE4kaYZDAasW7cOBoNBdClEOeHWhR8cfuXw6zxuHdFEDE6kaSMjIzh37hxGRkZEl0KUE8pKinC7yQgAeO5MHw+/QjQBgxNpmizL2Lt3L2RZFl0KUc5Q2xK8I1/Cm/3Dgqshyi0MTqRptbW12LZtG2pra0WXQpQz4tc5/fYMp+uI4jE4kaYVFxfDYDCguLhYdClEOWPJFQbUXV4GgOuciCZicCJNk2UZTzzxBKfqiOIUFelwR2N01Ol33X0YG+c6JyIVgxNpWlFREcrLy1FUxLcCUTx1ndNgKIIT7/CQREQqfluQplVVVWHDhg2oqqoSXQpRTllzwwfrnJ7jOieiGAYn0rRIJIKenh5EIjwSPFG8q6XLsKiuEgBwmMGJKIbBiTTN7/djz5498Pv9okshyjnqdN2xNwK4NDImuBqi3MDgRJpmNBpx//33w2g0ii6FKOesWRxt0zEyNg7PmzyeIxHA4EQaV1JSgoaGBh6rjiiJ2xfVQqeL/vuIj8etIwIYnEjjBgcHsX//fgwODoouhSjn1FSWYemV0R0nnmdwIgLA4EQaNz4+jlAohPHxcdGlEOWk1e8ft+74WzKGR7gTBRGDE2maJEm45557IEmS6FKIctJHTNF1TpFxheuciMDgRBo3NjaGYDCIsTHuMUSUTPw6p+e7OV1HxOBEmtbf349du3ahv59fCETJVFeUYtmC6DonLhAnYnAijZMkCVu2bOFUHdEUVr8/XffS2wMYCnOdE2kbgxNpWllZGRYuXIiysjLRpRDlrNVx65yOcZ0TaRyDE2laMBjEwYMHEQwGRZdClLM+vMjIfk5E72NwIk2LRCI4f/48j1VHNIXqy0qx/CqucyICGJxI42pqanDvvfeipqZGdClEOe0jceucLnKdE2kYgxNpmqIoGB0dhaIookshymnqOqexcQXH3uBBsUm7GJxI03p7e/Hwww+jt7dXdClEOe22RUYUxdY5MTiRdjE4kaZVV1dj8+bNqK6uFl0KUU6rKi/Fiquj7xMet460jMGJNE2v12PJkiXQ6/WiSyHKeep03Yl3BhAMjQquhkgMBifStKGhITz77LMYGhoSXQpRzvtIwjon9nMibWJwIk0Lh8M4deoUwuGw6FKIct6q62ti65xe4AJx0igGJ9I0o9GIrVu3wmg0ii6FKOcZyktx4/vHrTt6lsGJtInBiYiI0nbb9dEfGS+9PYDQ6Jjgaoiyj8GJNK23txcPPfQQ2xEQpUkNTiNj43jp7QHB1RBlH4MTaZrBYMD69ethMBhEl0KUF25b9EGX/aNc50QaxOBEmlZeXo6VK1eivLxcdClEeaHBUI7raysAAC9wnRNpEIMTadrw8DCOHDmC4eFh0aUQ5Q11us77ZgBj4zxcEWkLgxNp2vDwMA4fPszgRDQDanAKhiN49b1BwdUQZReDE2laXV0dHnjgAdTV1YkuhShv3Lbog/YdbEtAWsPgREREM3J9bQXqLo8epugoO4iTxjA4kab19fXhkUceQV9fn+hSiPKGTqfDbddH96574Q0/FIXrnEg7GJxI0yoqKrBmzRpUVFSILoUor6jrnHqDYbzZzzWCpB0MTqRpFRUVWL16NYMT0Qx9OH6dE/s5kYbkRHDyer3o6OhAa2srOjo6RJdDGhIKhXD8+HGEQiHRpRDllaVXGnC5vgQAgxNpi9Dg5HA40NjYCJvNBkmSYLfb0dbWlvSyHR0daG5uRnNzM5qamtDa2gpZlrNbMBWcYDCIAwcOIBgMii6FKK+UFBfh1oUSAC4QJ20pEXGjsixj7dq18Hq9aGlpQWdn55SXb2pqgizL8Hg8kCQJAGCz2bBo0SIcOnQIZrM5C1VTIaqvr8eOHTtEl0GUlz58vRG/fb0PZ/uG0BMMocHADvxU+LI+4iTLMpqamuD1etHZ2TltaLLZbPB6vXA6nbHQBABOpxMAsHbt2vksl4iIUojv5+ThqBNpRNaDU1NTE3w+H6xWK1paWqa8rNfrhcvlgslkSjqqtHHjRsiyDIfDMV/lUoHz+/3YvXs3/H6u0SCaqVuulVBarAMQbUtApAVZDU6tra3w+XyQJCk2YjSVnTt3AgAsFkvS85ubmwEAdrs9c0WSpuj1eixbtgx6vV50KUR5p7y0GMuvqgYAeM/JYoshypKsBaf4kaGNGzemtY3b7QYQHaVKRh2F8vl88Pl8GaiStKayshJ33XUXKisrRZdClJearos2wjz17gBCo2OCqyGaf1kLTvHTaepecY2NjdDpdGhqakJ7e3vC5X0+X2yvOZPJlPQ6jcYP5te9Xm/mi6aCFw6Hcfr0aYTDYdGlEOUl88JocBodU3DinQHB1RDNv6wFpyeffDL2b6fTiebmZnR2dsJut8Pn86GjowONjY2xsBQ/ghQfkOLFLxafbsSpp6cHJ0+eTPg7c+bM7O8QFYSBgQHs27cPAwP8wCeaDfN1Uuzfnje5QJwKX9baEagjQhaLBV1dXbHTLRYLrFYrGhsb4fP50N7ejs7OzoQeTfEBKZX+/v4pz9+9eze+853vzKp2Klz19fV48MEHUVIipDMHUd5bUH0ZFlSX4/xACN5zDE5U+LK+V526oDueyWSKNb50OByTGltO1ehSDVXTNcPcunUrTpw4kfD39NNPz6ByKkQ6nQ6lpaXQ6XSiSyHKW+p0nfeczAP+UsHLenBKNXq0adOm2L/VPe9UU+0qrgam6UalGhoasHz58oS/xYsXp1s2FahAIIDHHnsMgQB/KRPNlvn9BeK9wTDeDlwSXA3R/MpacJpuZCh+AfixY8ewatWq2P+nc2iVxsbGuZRHGlVSUoIFCxZwqo5oDszvH3oFAKfrqOBlLTipwSjVWqT4ESOTyQRJkmKnpRpxil8QnqrXE9FUDAYD1q1bB4PBILoUory1/KpqlJVEv068XCBOBS5rwUkdQUqnbYAastR+T93d3UkvpwYnSZJStiwgmsrIyAjOnTuHkZER0aUQ5a2ykiLcdDUbYZI2ZC04tba2AvigqeVEaggymUyxEDTdNureedu3b89oraQdsixj7969aU0HE1Fq6nTdK+cHcWmEjTCpcGUtOJnNZlitVgBAR0fHpPPVg/3GH/TXbDbDYrHA6/Um7dPkcDggSdK0x7wjSqW2thbbtm1DbW2t6FKI8praQTwyruClt2WxxRDNo6zuVffoo4/CYrGgvb0dLpcrdrrX64XD4UBbW9uktUpOpxNmsxk2my1hVEAdjTp06FBafZ6IkikuLobBYEBxcbHoUojymtqSAOB0HRW2rO5KJEkSurq60NHRgc7OTrS3t8cWgjudzqQLvCVJgsfjQUdHB2w2GyRJgizLMJlMOHv2LEMTzYksyzh48CDWrVvH1xLRHDRUleNq6TK8I1/innVU0HSKhruVnTx5EitWrMCJEyewfPly0eWQAIODgzh06BDWrl2Lqqoq0eUQ5bWvPfEifn78XdRWluHYX1vYWJYKUtYbYBLlkqqqKmzYsIGhiSgDmt5fIN4/NIJz/mGxxRDNEwYn0rRIJIKenh5EIhHRpRDlPbWDOMBGmFS4GJxI0/x+P/bs2TPlYX2IKD03LqhCeanaCFMWWwzRPGFwIk0zGo24//77YTQaRZdClPdKi4tw89USAI44UeFicCJNKykpQUNDA49VR5Qht7y/zun0e0GERtkIkwoPgxNp2uDgIPbv34/BwUHRpRAVhJXXSACijTBPvsv3FRUeBifStPHxcYRCIYyPj4suhaggrLy2Ovbv42/J4gohmiecnyBNkyQJ99xzj+gyiArG1dJlqLu8DH0XR3Cch16hAsQRJ9K0sbExBINBjI1xLQZRJuh0OtxyrQSAI05UmBicSNP6+/uxa9cu9Pf3iy6FqGCo65ze6B+GPDwithiiDGNwIk2TJAlbtmzhceqIMmjl+yNOAHD87QFxhRDNAwYn0rSysjIsXLgQZWVlokshKhg3X/PBAvE/nJPFFUI0DxicSNOCwSAOHjyIYDAouhSigiFVlGFRXSUAcIE4FRwGJ9K0SCSC8+fP81h1RBm28v1Rp+NvyVAURXA1RJnD4ESaVlNTg3vvvRc1NTXTX5iI0qauc+ofGsHbgUtiiyHKIAYn0jRFUTA6OspfxEQZlrhAXBZWB1GmMTiRpvX29uLhhx9Gb2+v6FKICsqyBVUoKdIBYD8nKiwMTqRp1dXV2Lx5M6qrq6e/MBGlrby0GDcuqAIAHH+LLQmocDA4kabp9XosWbIEer1edClEBUc9bt3L7wwgMsbjQVJhYHAiTRsaGsKzzz6LoaEh0aUQFRy1g/il0TG83nNRbDFEGcLgRJoWDodx6tQphMNh0aUQFZxb4heIc50TFQgGJ9I0o9GIrVu3wmg0ii6FqOCY6i9HRVkxgOh0HVEhYHAiIqJ5UVykw/KrogvETzA4UYFgcCJN6+3txUMPPcR2BETzZMXV0QXir7wXxCgXiFMBYHAiTTMYDFi/fj0MBoPoUogK0k3vB6eRyDheu8BjQlL+Y3AiTSsvL8fKlStRXl4uuhSigqQGJwB4+W1O11H+Y3AiTRseHsaRI0cwPDwsuhSigsQF4lRoGJxI04aHh3H48GEGJ6J5wgXiVGgYnEjT6urq8MADD6Curk50KUQFiwvEqZAwOBER0bziAnEqJAxOpGl9fX145JFH0NfXJ7oUooIVv0Cc03WU7xicSNMqKiqwZs0aVFRUiC6FqGDFLxB/iXvWUZ5jcCJNq6iowOrVqxmciOYRF4hTIWFwIk0LhUI4fvw4QqGQ6FKIChoXiFOhYHAiTQsGgzhw4ACCQS5YJZpPXCBOhYLBiTStvr4eO3bsQH19vehSiAoaF4hToWBwIiKieccF4lQoGJxI0/x+P3bv3g2/3y+6FKKCxgXiVCgYnEjT9Ho9li1bBr1eL7oUooLHBeJUCBicSNMqKytx1113obKyUnQpRAVv+VUfLBDv7r0ouBqi2WFwIk0Lh8M4ffo0wuGw6FKICt6yBVWxf596d1BgJUSzx+BEmjYwMIB9+/ZhYIBrLojm2+KGy1FWHP3aYXCifMXgRJpWX1+PBx98kO0IiLKgrKQIN1xxOQDgJIMT5SkGJ9I0nU6H0tJS6HQ60aUQaYK6Z92p84NQFEVwNUQzx+BEmhYIBPDYY48hEAiILoVIE9R1TgOXRvGOfElwNUQzx+BEmlZSUoIFCxagpKREdClEmrDsqg86iHOdE+UjBifSNIPBgHXr1sFgMIguhUgTblzwwXvt1HkGJ8o/DE6kaSMjIzh37hxGRkZEl0KkCYbyUlxXWwGAC8QpPzE4kabJsoy9e/dClmXRpRBphrrOiVN1lI8YnEjTamtrsW3bNtTW1oouhaggNTY2TjpN3bPuHfkS5GGO9uYKr9eLjo4ONDU1JX3eKIrBiTStuLgYBoMBxcXFokshKjgOhwM+nw8OhyPh9GVXxXUQ5zqnnOD1euF2u7Fz5054vV7R5eQ0BifSNFmW8cQTT3Cqjmge2O32hP+qli3I7T3rtPh5YDab0dbWhu3bt4suJecxOJGmFRUVoby8HEVFfCsQZZLb7YbP5wMA+Hw+uN3u2HlXVOlRW1kGIDeD09q1azUZngBAkiTRJeQ8fluQplVVVWHDhg2oqqqa/sJElDa73Q673R77Io4fddLpdLHpulybqmtvb+dUFU2JwYk0LRKJoKenB5FIRHQpRAVDHWFqaWmJTf3Ej0ABH6xzer3nIkKjY0LqnMjhcKCjo0N0GZTjGJxI0/x+P/bs2QO/3y+6FKKC0dnZiZaWFkiShJaWltjp8aNOakuCsXEFr1+4GDvd5/OhubkZzc3NsNlsaGxsRGNjY8JU30wup15WvUxNTQ2am5snXc7lcqG9vT32/+qeZTabLe37PV1NLpcLTU1N0Ol00Ol0CdOB7e3taGxsjJ0XT5ZlOBwONDU1wev1xu5PTU0NampqYLPZJk0tzmabVFwuF2pqamK1NTU1weVyJVymo6MDOp0ONTU1k86bDVmW0draiqamJrS2tsJms6G9vR2tra1zvu45UzTsxIkTCgDlxIkTokshQUZHR5ULFy4oo6OjokshKhiSJCkejyf2/1arVQGgxH/lvH5hULmu/YByXfsBZd8LbyqKoijd3d0KAKWrqyt2ubmcpiiK0tnZqVgsFqW7u1tRFEXxeDyKyWRSACh2uz3hsup1AFACgcCM7nO6NXk8npS30dXVNelxUhRFsdvtsdPtdrsiSZJitVoVi8USO12SpNh9nO026uMFQDGZTCkfm87OzqSPgclkSnneTKl1qo9RIBBQJEma9JyJwODE4ERElDGdnZ2K2WxOOC0+LKhffJGxcWXJXz+jXNd+QPmbp19WFEVR2trakgYKu92uOJ3O2P+nezk1iEy8nNPpjNUTHxzmEpzSrWmq24g/byL1dIvFknC6x+NRJElKet5stkkVnBTlgzAzcZv42jMl2ePQ1taW8FiKwqk60rTBwUHs378fg4O5tUCVKF/Z7fZJu7SbzWaYzWYA0Wk8ACgu0mHJFdHj1r36XhDAB20Adu7cmbC91WqFyWSK/X+6l2tvb4fFYpm0p5jFYon9W61nrtKtyWg0zul2JrZ2MJvNKdeRzWWbZNSpzGTbuFwuWK3W9O7EDMRP+23atCn2OhKJwYk0bXx8HKFQCOPj46JLIcp7brcbfr8/6Reo+kXt8/liX4ZLr4yuc3r1/CAURYmtJ+ro6EBzc3Psy9lkMiV8YaZzOVmWY00d1bVG6l9TUxNMJhNMJlPG2g6kW/t8iF9Hlu4egbPZxmKxxO7LxDDW2dmZ0fVHan333Xdf7Dkym80JIVQY0UNeInGqjogoc+LXMk31p07lPfacL7bO6V15WFGUxLU5AJSWlpak02bTXU6dprNarWnXP5epunRrDwQCc5qqi187Fk+deotfYzSbbaaaqlOUxGlOtf7u7m5FkqSkl58tdU0TUkwNisQRJ9K0sbExBINBjI3lxu7QRPlKlmW4XC54PB4EAoGkf+oogtfrhdfrxZIrDbHtXz0fna5ra2tDV1dXbGTB4XBg0aJFk0ZF0r1cutNQmZBuTfNBnQKcyYjMbLaJn3pUD6Wj7kWZSZIk4dFHHwUQHcnMxJ56mcLgRJrW39+PXbt2ob+/X3QpRHlt586dsakcSZKS/sXv7r9z587YVB0AvPLeB+sMLRYLuru70dnZCUmSIMsy1q5dO+k2p7qc+uXu9XqnnI7LdIfwdGvPNDUgrlq1al63AT5Y66Su53K5XBmbpot/PqxWa2w92sS1YyIxOJGmSZKELVu28DADRHPkcDim/fKMX+/jcrlQNDqMK6r0AIDT7wUnbd/S0gKPxwMg+oWqftGnc7mJi8RTue+++9K5e9NKt/Z4E/vHzTbEqdethtb52kal9uhSey1JkpSRtUc+n29SQFLXUuVSN3cGJ9K0srIyLFy4EGVlZaJLIcpbHR0dkGU5rb2q4gPGzp07sSS2QDw6VTdxSsZkMsVGHeK/nNO5XFtbG4BoqJsYnrxeLxobGxPqid/jLT7ozKRR5HQ1xYeUiU044/fwS3WbyZr1qtupU1uZ2GY66mL/6QJze3s7bDZbWlOmE49pCCAWtHNiUbhK9CIrkbg4nAYHB5VnnnlGGRwcFF0KUV5SF/Gmuzg4vskjAOVvnjyiXNd+QGnc/gvlz//ivqRNGSVJUtra2mL/39LSktblAoGAYjabE27PZDLFFh0n6wmkNsc0m82x5pnpLBRPtyb1smo9VqtVaWlpUUwmU8JjY7VaExZ1x9cff7q6mDtZ48nZbKP2o0rn+QSmXkiv3g6mWGye7PLJenFlqrFmJjA4MThpmt/vV37wgx8ofr9fdClEecfpdMaCBt7f+2mqBoVtbW0JlwegVBqqFeOnv6pc135A+fOt31CsVmusy7XVao0FmInXk87l4i+vBiiTyaRYrdZJAUeldhZXrzvV5ZLdxkxrUgNn/O2YTCalra1t0p5w6uPldDoVi8USq9FisaTca24m23g8nlhoig9vUz2f6v1Mpbu7O+H5ni6A2u12pa2tTWlpaYn912w250TTy3g6RVGU+RrNynUnT57EihUrcOLECSxfvlx0OUREmvPK+UGs+9+/BQD806aV2HDrNYIryk3q8es8Hk/afaFms81MNDY2orOzM6GhaCo1NTUIBAIZr0EErnEiTVMUBaOjo9Dw7wcioRrrL0dJUfQLXl3nRLlPXaydTmhyu93YuHHjfJeUNQxOpGm9vb14+OGH0dvbK7oUIk0qKylCY/3lAD449Arlvp07d6bVgsDr9cJut2fs0Da5oER0AUQiVVdXY/PmzaiurhZdCpFmLV1gwOkLQbwa18uJkku2h9x8bDNRR0cHuru70dzcjKNHj8LtdsPpdE67ndvtRldX15xvP5cwOJGm6fV6LFmyRHQZRJqmdhC/MBhGYGgENZVsDxKvo6Mj9u/Ozk6sWrVq2t5Ls9kmFZ/PF2vnoHYLTzcMqS0hCgmn6kjThoaG8Oyzz2JoaEh0KUSadWNcB3FO1yWy2Wx48sknYTabYTab4fP5sHbt2liAydQ2UzGZTGhra4s1uuzq6kprbVOhyqkRJ1mWcezYMU0/IZRd4XAYp06dws0334zKykrR5RBp0tIFccese28QH2msFVhNbklnOiwT20zHbrfHunhrnZARp9bWVuh0ukl/NTU1CZ1b43V0dKC5uRnNzc1oampCa2trxo8xRNpjNBqxdevWlK87Ipp/V1aVo6o8+juee9ZRrsv6iJMsyymHC9VhxYmampogyzI8Hk9sjtZms2HRokU4dOjQvPSnICKi7NDpdFi6oAovnPXj1QsMTpTbsj7itHPnTrS0tCAQCEz6Uw+IGM9ms8Hr9cLpdCYsbFOHIrNx1GkqXL29vXjooYfYjoBIsBvfXyD+2ntBjI2zrxrlrqwHJ/Vgi5IkTfqbyOv1wuVyJRxRO97GjRunHMEimo7BYMD69ethMBimvzARzRv1YL+XRsdwzj8suBqi1LIanNTdI10uV6zr6FR27twJIHVn0ubmZgDggjWatfLycqxcuRLl5eWiSyHSNLUlAQCc5p51lMOyGpx27twJWZbR3t6OpqYm1NTUTLnI2+12A4iucUpGHYXy+Xzw+XzzUjMVtuHhYRw5cgTDw/yFSyTSh664PPbv17jOiXJY1haH+3w+rFq1KiHkqNNsDodjUl8In88XC1QmkynpdcbvCeX1elNejiiV4eFhHD58GIsXL0ZFRYXocog0y1Beiquly/COfAm7ul7DqXfZRbyQSRWl+Mc/uVl0GbOSteCkNs0CooHpqaeegt1uj4Wo5ubmhCM4x48gpdpVPH5d1HQjTj09PZMWAJ85cwYAMDAwEDutt7cXBoMB5eXlGB4exvDwMOrq6gAAfX19qKioQEVFBUKhEILBIOrr6wFEW9rr9XpUVlYiHA5jYGAA9fX10Ol0CAQCKCkpgcFgwMjICGRZRm1tLYqLiyHLMoqKilBVVYVIJAK/3w+j0YiSkhIMDg5ifHwckiRhbGwM/f39kCQJZWVlCAaDiEQiqKmpgaIo6O3tRXV1NfR6PYaGhhAOh2OPG+9T6vs0Pj6Obdu2FdR9KsTnifdJG/fpjsZa/MLjwzh0+M+T7035mU757YoqvegSZk1IHydJktDS0oLu7u6E9Un33Xdf7N/x03fptInv7++f8vzdu3djxYoVCX933303AOCZZ56JXc7hcOD06dMAgJdeegmPP/547LzHH38cL730EgDg9OnTCYvS9+3bh6NHjwIA3njjDezZsweRSAQAsH//fjz33HMAgPfeew979uyJTQ0dPHgQhw4dAhD9ENqzZ0/suEKHDh3CwYMHAURHRvbs2YP33ot+mDz33HPYv38/ACASiWDPnj144403AABHjx7Fvn37eJ94n3ifeJ/y6j7t+KMb8bkrA7izegBLrjDwr4D/1AM75yUlB9jtdgWAEl+O0+mMnebxeFJuK0mSAkBpaWmZ8jYuXLignDhxIuHv6aefVgAohw8fjl2up6dHuXTpkqIoijI0NKT09vbGzuvt7VWGhoYURVGUS5cuKT09PbHz+vv7lYsXLyqKoiihUEi5cOGCMj4+riiKovj9fmVwcFBRFEUJh8PKhQsXlEgkoiiKogQCAWVgYEBRFEUZHR1VLly4oIyOjiqKoigDAwNKIBBQFEVRIpGIcuHCBSUcDiuKoiiDg4OK3+9XFEVRxsfHlQsXLiihUEhRFEW5ePGi0t/fz/uUxn169dVXle9+97tKb29vwdynQnyeeJ+0c5/Onj2rdHR0JNRAlEt0iqLkRMOMmpqaWJNLs9kMt9sd22tuquPi6HQ6ANEDCc5077qTJ09ixYoVOHHiBJYvXz63O0B5aXh4GC+99BJuvvlmrnEiygF8T1IoFMLvf/97rF69Gnp97k3p5cxBftVgpA5Br1q1KnZeOodWaWxsnJe6qLBVVFRg9erV/IAmyhF8T9KPfvQj3HXXXVi8eDE6OzsxMjIiuqQEOROc1EWKamCKb4qphqmJ4heE88DANBuhUAjHjx9HKBQSXQoRge9J+mCHrbfffhtf+cpX0NjYmFMBKmeCkxqC4heCb9y4EQDQ3d097TZsRUCzEQwGceDAAQSD7BtDlAv4nqSJ3nnnnZwKUDkTnI4dOzZpjVJrayuADxphTqS2N9i+ffv8FkcFq76+Hjt27IjtXk1EYvE9SROpS7HVACV6Ci9rwamjowPt7e1J1yu1t7fDZDKhra0t4XSz2QyLxQKv15u0T5PD4Yi1NiAiIqLcMjIygnA4PKM/taXFRGqAip/C+973vodwOJzNu4Ss7FUnyzJqampi/2+329HW1gafz4fOzk7Isgy73Z60X5Msy1i7di2AaM8S9TKtra146qmncOjQoaQHAE6H1+tFU1MTnn76aSxevHhW10H5bWBgAM888ww+85nPoLq6WnQ5RJrH92ThcDqd+M53vjPvt1NfX4+enp55vx1V1toRuFwu7Ny5M3ZwX5PJBLPZjNbW1rQWdnd0dKCrqwuSJEGWZZhMppRhK12PP/44vvzlL896eyIiIpq95uZmNDQ0JJz2yiuvxLLCdEpKSnDPPffghz/84XyUl1TO9HES4fnnn8cdd9yBp556CsuWLRNdDglw5swZ3H333Rx1JMoRfE9qg/o8xx9qTbVr1y488MADU25/+eWX45vf/Ca+9a1vpTws23zJ2rHqclFVVRUAYNmyZWyAqXGLFy/ma4Aoh/A9qQ0zbXApMjCpNB2ciIiIKPflQmBSMTgRERFRTrr88svxjW98A9/61rdQW1sruhwADE5ERESUQ2677TY0NjZi8+bNORWYVJoOTvX19fi7v/s7NlrTML4GiHIL35PaMNXz/NGPfhRnzpwRUFV6NL1XHREREdFM5MwhV4iIiIhyHYMTERERUZoYnIiIiIjSxOBERERElCYGpxno6OiAzWZDY2MjAMDn86G5uRk1NTXw+XwAALfbjcbGRjQ1NYkslYiIiOYBg9MMtLW1we12w2QywefzobOzE+3t7ZBlGW63Gy6XC7Isw2q1wuv1xsIUERFRrnG5XPyRPwsMTjMgyzJkWUZzczNcLhfsdvuky1itVsiyDEmSYDKZUl5XR0cHmpub0z4CNBERUSZ4vV40NTXBZrPxB/4sMDjNgNvtBgD09/ejra0NANDV1QVJkmIjTerlLBbLlNdlsVjgdrsnHRWaiIhovnR0dODJJ5+ELMvzcv3q92Qh03Tn8Jnq6uoCADQ3N8dOc7vdkGUZLS0tAKKjUj6fD+3t7VNeF0PT3MmyjPb2dhw7dgyrVq2C3++HyWSCLMvo7OwUXR6R5vA9mfvUH/2NjY1obW3N6HV7vV60traiu7s7o9ebazQTnNIZBUrnOsxmc+x6ZFmG1+tFW1sbJEmKXQbAtLfV1dU153q0zmazwe12IxAIxEb9Fi1ahO3bt4sujUiT+J7MH0ajUXQJeUsTU3VqCp4Ln88Hn8+XcD1qSNq0aVPsNHXqzmQyTVq/5HA44PP54HK54Ha7E7ajmVMffzW0SpKElpaWhLVl6sL9jo4Oricjmmd8T5IWCAtObrcbDocjYZ5VDSZNTU1obm6Gw+EQVd4k6gfCxo0bY6epISl+yu3YsWOwWCxwuVyx02RZRlNTEywWC0wmE/x+PwBwqi5D4h/rTZs2JTyufr8/tvfjfHA4HAlTt0Qk5j3p8/lgs9nQ2tqK1tbWeXvP0/zxer1wOByxHBCfAdrb21FTU4OmpqaE3OByudDY2Iiampqsra/KenBSQ0RzczNaW1tjPZDU/kcOhwNerxdutzunXvzd3d2wWq2xX1JA9ANAXdukMhqN8Hq9CYHKZrNh06ZNsV9dHo8ntpCcZk997O+7777YG8lsNif8ujWZTDCZTJMC7ly53W60t7ejtbWVe6UQvU/Ue9Ln88W+Vzo7O9HZ2RnbA5ryiyzLsRygLmdRn0f1+1UNSO3t7XjyySdhNpshy3LSPd3nhZJlVqtV6e7uVtra2hQACgClpaVFMZvNisfjURRFUbq6uhRJkmLnz5XH41FMJtOcr2c21PsST5IkpbOzU0g9hSQQCMReJxaLJeXlTCaTYrVa56UGk8kk7LVFlGtEvSetVuukz9lAIKAA4GdtCk6nUwEw6XGbi0x813o8HgVA7HpaWlqU7u5uRVEUxWKxKACUrq4upbOzM/bcqvdlqtecqqurS7Hb7QnXO1NZHXHy+XyxXxvxvwR8Ph88Hk/s14fFYkkYyZmv3Sazwel0YtWqVbH/V5tkxk/50exIkoRHH30UAGINSCdS93LkL0+i+SfiPSnLMlwu16TPVHWtadZGISgj4new6ujoQHt7e2zE8tixY7HLxe/NnmyP91QsFguOHj0Kh8MxZa/FqWR9qi7Z3hVOp3PSabW1tbF/x0+P5Rt1PRMQnb998sknY08Wp3hmJz5IW63W2HDuzp07J112ur0c1d2n29vbY01J+bwQzYzI96R6feqhsOKZzWb4fL68/vGtNWoIUoOv+n3p9XohyzLMZjOcTmesrQKQ/t7sKvV6ZiurwUmd1wY+CA2SJCUNRmofiNkmwlyxffv22Py7z+eL7Ul37NixvL9vIvh8vkkfxuovymR76MTv5TiR2j23ubkZdrs99kbk6BRR+kS/J+O/SyZSd7nnj6HMcLlc0Ol0Kf/U77mpLlNTUzPlbaghKL6pdPzpE/duV0cwZ7Jmbq7tiYT1cfJ4PACQMI0VTx2Sy5U9z3Q63Zyvw2azxf498YNAUZQ5X78WqDsSxFNfI8k+iFO9QWRZxtq1a9HS0pJwvtlsjjU1zeeRTqJsEf2e7O/vBzB1X6L4kX+aPavVOuV3ldfrhc1mm3UDzPjX0cQdw9SRKIvFkpALZjrapF5+Lj+QhbUjUH8BpBp1Uc+/7bbbpr2ubKRgyg0+nw9erzdh7UT8Hhbx1F8iyfpl2Ww2yLI8aeqYv0yJZkb0e1KdopsqHLHZY36ID0cTs4H6mpq4Zi2d9U2tra2w2Wyw2WyxpUF5OeKkjiglOzKzejBdIL2puvlOwQBHhHKFLMtoa2tDV1cXjh49ClmWcezYMTidzkktHlL9ElEb8LW0tEz6Bev1ehOmlIloaqLfk+qsRbJ1TGqY4rKI/KC+PiY2rFZPn9jaIv48deeA+JFJtf2R3W6H1WqFLMuoqamZ81E7hASn+GCUbKoufuV8rkzVUW6IXxA4HXUh/sQPXPWNNjG0u91u+Hy+pDsrEFFyot+T6hdpsh/G6p7c/CGU+9RDmAGYFLjVUaWJI5XqCKb6HKt7ylksltjhfjZu3Bi7PjVIz3Udq5CpuvhglGqB4FTnE6Ujfi1F/GtKffNMDO3qrxI2JyWaH/PxnpQkCVarddI6K/WLONMHsi0UR48eBZA77X7U5y/Zc51qpDI+S3R0dCRcRp0mjp/aU6d95/oZLyQ4FeoedeoeITqdDo2NjbEnkrJP3QVZPXRP/GtJfWPFv+nU1v4Tf9mqa+Om6mDPhadE05vP96Tdboff709YZ7Vz506YTKYZjYhpgcvlirV6UDU3N6Ojo0PoGk+fzwez2ZzyGK4TF4UD0aCtZgiTyRTr6+Tz+eBwOLB9+/aEjOF0OlPu0Tkjs2qbOUctLS1Tdvk0m80KgIx1ls1G53Cn06mYTCalra1NaWlpiXXPbWlpmdfbpdQkSVLMZnPS7rBOp1Mxm82K3W5P6EA7kdoJ2Ww2J5zu8XgSut/b7fZY53siSm4+35OBQECxWq2xz+CWlhYlEAjMx92gFEQepSOe+tkc/zpTO8ln4jtZSHBSg1GqO6CGDrvdnpHby8aTOTEExh96gG/e/Jep1yIRZQbfk7mnu7s7rcOezDer1TrpcG3qgI3T6VQURZnT97KQqTqPxwNFUdDZ2TnpvJnuUZcLvF7vpF0kJUmK7VYbP/xMRERUiEwmU2wht0hq+wl16rajoyPWtkJdRD6XHQaEtSNIZT72qMvInOYUUtUZP/dK+UvdHZqIcgPfkzQVu92OY8eOwWazwWKxoLOzM9amwOl0Jh20mYmcC07xi9My9cYQlYK7urqS9p2g/OJ2u7nAlCiH8D1JU5EkKXZ0kniBQCAj159zwSlf96ibSG3odujQIdGl0BzxA5oot/A9SSIJO+RKKrnSU2Ku2tvbYbfb2cCTiIiogORccFLXBeVzbxyHw4HGxsZYTwkiIiIqDDpF4UHYMsntdsPr9XIomYiIqADl3IhTPksVmnw+n9COrERERJQZHHHKEK/XC5vNNum4SP39/fB6vTnR24KIiIjmhsEpA9Rj1KXS2dnJ9U5EREQFgMGJiIiIKE1c40RERESUJgYnIiIiojQxOBERERGlicGJiIiIKE0MTkRERERpYnAiIiIiShODExEREVGaGJyIiIiI0sTgRERERJQmBiciIiKiNP3/AaLLnm5sCLo+AAAAAElFTkSuQmCC",
      "text/plain": [
       "Figure(PyObject <Figure size 600x600 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject Text(0.7, 0.05, '\\\\shortstack[l]{Asset supply, $s$}')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Figure 5(a): supply shock on s\n",
    "\n",
    "## range of asset supply, s\n",
    "mD = 0.1 # dealer size\n",
    "xRange = range(0.5+mD/2,1.0+mD-1e-3,length=800) # focusing on excess supply\n",
    "\n",
    "## initialize output\n",
    "di = Dict()\n",
    "\n",
    "## compute\n",
    "guess=[0.99,0.99,] # set some initial guess\n",
    "for x in xRange\n",
    "    diPara = Dict(\n",
    "        :s  => x,:mD => 0.1, # update s\n",
    "        :λu => 0.1,:λd => 0.1,:r=>0.05,\n",
    "        :values => (1.0, 0.9, 0.0), # yh, yd, yl\n",
    "        :searchB => (3.0, 1, 0.5), # ρB, nB, qB\n",
    "        :searchS => (3.0, 10, 0.0), # ρS, nS, qS\n",
    "    )\n",
    "    converging,θLO,θHN,diB,diS,diX = solveEquilibrium(diPara,guess=guess)\n",
    "    di[x] = Dict(\n",
    "        :converging=>converging,\n",
    "        :θLO=>round(θLO,digits=5),:θHN=>round(θHN,digits=5),\n",
    "        :diB=>diB,:diS=>diS,:diX=>diX\n",
    "    )\n",
    "    guess = converging ? [θLO,θHN,] : rand(2) # recursively update guess based on the previous eqm\n",
    "end\n",
    "\n",
    "## find thresholds for s\n",
    "s2p = xRange[findlast([di[x][:θLO] for x in xRange] .== 1.0)]\n",
    "s1p = xRange[findfirst([di[x][:θLO] for x in xRange] .== 0.0)]\n",
    "\n",
    "## set up canvas\n",
    "fig = PyPlot.figure(figsize=(5,5), facecolor=\"w\", dpi=120) # create figure\n",
    "fig.subplots_adjust(left=.1, right=.9, bottom=0.13, top=.93) # reduce white spaces\n",
    "ax = fig.add_subplot(111) # create axis for total trading cost\n",
    "\n",
    "## plot SMS volume ratio\n",
    "ax.plot(xRange, [di[x][:converging] ? di[x][:diS][:volume] ./ (di[x][:diB][:volume]+di[x][:diS][:volume]) * 1e2 : NaN for x in xRange], ls=\"-\")\n",
    "\n",
    "## reference lines\n",
    "ax.vlines([s2p,],ymin=49.0,ymax=100.0,linestyle=\":\",lw=0.8,colors=\"gray\")\n",
    "ax.vlines([s1p,],ymin=49.0,ymax=50.0,linestyle=\":\",lw=0.8,colors=\"gray\")\n",
    "ax.hlines([50.0,],xmin=minimum(xRange),xmax=s1p,linestyle=\":\",lw=0.8,colors=\"gray\")\n",
    "\n",
    "## set axes ticks and ranges\n",
    "ax.set_xlim([minimum(xRange), 1.0 + mD])\n",
    "ax.set_ylim([49.0,1e2+1])\n",
    "ax.set_yticks(50:10:100)\n",
    "ax.set_xticks([minimum(xRange),s2p,s1p,1.0+mD])\n",
    "ax.set_xticklabels([L\"$\\eta+\\frac{m_d}{2}$\",L\"$s_{lo,1}$\",L\"$s_{lo,0}$\",L\"$1+m_d$\"])\n",
    "\n",
    "## format axes\n",
    "ax.spines[\"right\"].set_visible(false)\n",
    "ax.spines[\"top\"].set_visible(false)\n",
    "arrowed_spines(ax) # add arrows\n",
    "\n",
    "## label axis next to arrows\n",
    "ax.annotate(L\"\\shortstack[l]{SMS volume relative to total volume, \\%}\", xy=(0.02,1.0,), xycoords=\"axes fraction\") # left y label\n",
    "ax.annotate(L\"\\shortstack[l]{Asset supply, $s$}\", xy=(0.7,0.05), xycoords=\"axes fraction\") # x-axis label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ly89mGyIiolwgTccn5ZjVmKFkCjxJpcMTVcVsbGwEEL0QYCrbEBEREaUi40UXpWJZiQZZSy0/Upn3VLeZqerq6pS2IyIiovyS0WTI4/GExxUlKr8e2brkdrtT2iYVf/EXf4Gf//znKW1LRERE+SPl2WTJiGy1SdSlFjk42uPxRP0/2W2S0d3djZ6enqjbpBJLX/z+UZQ19MTbLKf95h8+iFI1V1QhIiKajYwmQ5GzzZKZfh87Qy3ZbZKxc+dOfO1rX4t73+t9QygtGkxqP7mgDGO4Wd2NwUEjSqu0codDRESkaBlNhiJNNQ1fWhE59jGpbJPI1q1bJy1ceubMGdx5550AgJuv0eKmq5WRWKiCw5jjHQDECblDISIiUryMJkORLTtTrWotJTSCIKS0TTIaGhrQ0NCQ8H7LyuvxhfcvTGpfueF9CI4zGSIiIpqtjA44WblyZfjfybTg6PX6lLZJh6o5JWnZTzY8c+rP+Px//Qq3fu0ABobH5A6HiIhI0TKaDEW29CRq5YkcAG0ymVLaJh2UlAxdDPRjofcoiscu4cXzfrnDISIiUrSMT0WSFqrr6uqKe7+U2AiCEJ5Kn8o2s1VZlrXhU7N2+5IF2HNpOfpFDVznEnclEhER0fQyngw1NTUBuFJIMZa0fsv27dtntc1sVZUrp2WoVjsH8xpqIKIIx17n+mxERESzkfFkyGAwwGQyhQsqxnI4HBAEAVardVbbzFZlmXKSIb/fj/fhFVSoRvDSG34OpCYiIpqFWSVDR48eBTD9QGen0wmDwQCLxRL1WKkF6ODBg5NmhaWyzWwoacxQUVERaiqvgiiqMDQ6jpcvBOQOiYiISLFSGijT1taGo0ePorW1NXxbY2MjGhsbYTabJ43jEQQBLpcLra2tsFgs4RpBOp0OZ8+ejZvUpLJNqtRFKpSXFqdtf5lWWVmJT915J/7jG4cAAK7XvXjXvCp5gyIiIlIolSitSVGATp48iWXLluGmrXac/lb6utwyLRgMoq+vD+vsL6FnKIh1y+fivz9jkDssIiIiReLCVgDKS5UzkwwIlRz49re/jfdcWwoAcHEQNRERUcqYDAGYU6qsy1BTU4P7778fK/TXAQAu9A/jLf8lmaMiIiJSJmVlARlSVqKc8UIAoFar0dDQgJW6+vBtx1hviIiIKCVMhgCUlyirm2xgYAD79u3DfC1Qqg49hewqIyIiSg2TISivm2xiYgLDw8MoKVJhxeVZZMfOMRkiIiJKhbKygAyZo7CWIUEQsHnzZgiCAOMNNQCAV94ewMWRoMyRERERKQ+TIUBRNYYAYHx8HIFAAOPj41h5QzUAYEIEXjzP1iEiIqKZYjIEoExh3WR9fX147LHH0NfXB+PlZAhgVxkREVEqlJUFZIjSBlALgoAtW7ZAEARUV5RCX18BgIOoiYiIUsFkCMAchXWTlZaWYv78+SgtDRVdlFqHXjzvw/hEwRYUJyIiSgmTIQBzFFZnKBAI4MCBAwgEQgu0rrw8iHpwdByvvD0gZ2hERESKw2QIymsZCgaDuHDhAoLB0Owx4wKOGyIiIkoVkyEoLxmqrq7Gvffei+rqUBKkq6tA3VWhLrMXzrISNRER0UwwGYLyBlCLooixsTGIYmh8kEqlwm0LQ11lz5/tC99ORERE02MyBOVVoO7p6cEjjzyCnp6e8G3vXVgLAOi9OApP76BcoRERESmOsrKADFHaAOqqqips2rQJVVVV4dukliEAeN7DrjIiIqJkMRkCMKdUWd1kGo0GixcvhkajCd+2+GotquaUAABeONsnV2hERESKw2QIyluOY3BwEIcOHcLg4JXusKIiFd6zQBo35OW4ISIioiQxGQKgUSvrMoyMjODUqVMYGRmJun2VLpQMXegfxpu+S3KERkREpDjKygIyRKVSyR3CjNTU1GDr1q2oqamJul0aRA0ARzzsKiMiIkoGk6E8cstcLa7ShMY/Pc96Q0RERElhMqRAPT09ePjhh6Om1gOAurgIKy9Xo2bxRSIiouQwGVIgrVaLdevWQavVTrpP6io77x3ChX6OGyIiIpoOkyEFKisrw4oVK1BWVjbpvsh6Q2wdIiIimh6TIQUaGhrCkSNHMDQ0NOm+d11XFS4ieYTFF4mIiKbFZEiBhoaGcPjw4bjJUKm6CIYbBAAsvkhERJQMJkMKVFdXh23btqGuri7u/dK4oa6eQfQERuI+hoiIiEKYDOWh93LcEBERUdKYDClQb28vHn30UfT29sa9f8X1AkovV9V+nl1lREREU2IypEDl5eVYvXo1ysvL495fVlKMW68XAADPdTEZIiIimgqTIQUqLy/HqlWrEiZDALB6UWg80WvdF9E9MJyt0IiIiBSHyZACDQ8P4/jx4xgeTpzk3K6/sk7Zc1ynjIiIKCEmQwoUCASwf/9+BAKBhI9ZPk9AeWmo3tAfzzAZIiIiSoTJkALV19fjoYceQn19fcLHlKqLwtWoD3fFH2hNRERETIbymtRV9qbvEt7wTi7QSEREREyGFMnr9WLnzp3weqeuIXS7/kpRxsNn2DpEREQUD5MhBdJoNFiyZAk0Gs2Uj1sytxJVc0oAAH/kFHsiIqK4mAwpUEVFBe644w5UVFRM+biiIhXepwt1lf2xqw+iKGYjPCIiIkVhMqRAIyMjOH36NEZGpl937PZFoWSo9+IIXuu+mOnQiIiIFIfJkAL19/djz5496O/vn/axkeOG/shxQ0RERJMwGVKg+vp6PPjgg1NOrZfo6yvQoA2NLTrMcUNERESTMBlSIJVKhZKSEqhUqqQeK02xP+Lpw/gExw0RERFFYjKkQD6fD0888QR8Pl9Sj7/98jplgeEgOt+avmuNiIiokDAZUiC1Wo25c+dCrVYn9fjIdco4xZ6IiCgakyEF0mq1WLt2LbRabVKPn1ddjvk1oRXu/8ilOYiIiKIwGVKg0dFRnD9/HqOjo0lvs/ryFPsXznoxPDaeqdCIiIgUh8mQAvn9fuzevRt+vz/pbT5wY2jm2UhwAkfPTb2MBxERUSFhMqRAtbW1eOCBB1BbWzv9gy9bra9D0eXJZ394jV1lREREEiZDClRcXAytVovi4uKkt6kqL8GK6wUAwO9f7clQZERERMrDZEiB/H4/nnzyyRl1kwFXuspeeTuA7oHhDERGRESkPEyGFKioqAhlZWUoKprZ0/fBG68szfEsl+YgIiICwGRIkSorK7F+/XpUVlbOaLsV1wvQakK1idhVRkREFMJkSIGCwSC6u7sRDAZntF1JcVF4Fftnz/RigktzEBERMRlSIq/Xi127dsHrnfkUeWncUO/FUbz89kC6QyMiIlIcJkMKVFNTg/vvvx81NTUz3vaDN15Z6Z5T7ImIiJgMKZJarUZDQ0PSa5NFml9bjhtqQ0tz/OE1jhsiIiJiMqRAAwMD2LdvHwYGUuvm+sDlWWVHz/owNDqzcUdERET5hsmQAk1MTGB4eBgTExMpbS91lY2OT+D5s1yag4iIChuTIQUSBAGbN2+GIAgpbf8+fS2KL6/N8YdXOW6IiIgKG5MhBRofH0cgEMD4eGqrz2vLSmCYLwDguCEiIiImQwrU19eHxx57DH19fSnvQ5pi/1r3Rbzlv5Su0IiIiBSHyZACCYKALVu2pNxNBgB3LL4yxf7Q6e40REVERKRMTIYUqLS0FPPnz0dpaWnK+1h2bRXqrtIAAJ55hV1lRERUuJgMKVAgEMCBAwcQCARS3kdRkSrcOnT4TC9GgqmNPyIiIlI6JkMKFAwGceHChRmvTRbrQ4sbAACXxsbxAqfYExFRgWIypEDV1dW49957UV1dPav9vP/GuvAUe3aVERFRoWIypECiKGJsbAyiOLtV56vmlMB4Qyih4iBqIiIqVEyGFKinpwePPPIIenpm35ojdZV5egdxrndw1vsjIiJSGiZDClRVVYVNmzahqqpq1vv60M2cYk9ERIWNyZACaTQaLF68GBqNZtb7Wny1FnOrygAAz5zmuCEiIio8TIYUaHBwEIcOHcLg4Oy7tVQqFe643FX2nKcPl0Y5xZ6IiAoLkyEFGhkZwalTpzAyMpKW/X3ocr2h0eAEnvNw4VYiIiosWU+G/H4/mpqa0NjYCL1eD71ej9bW1mm3a21tRWNjIxobG2E0GtHU1AS/35/5gHNQTU0Ntm7dipqamrTsb/WiOpQWh14KnGJPRESFJqvJUEdHBxYuXAij0Yj29nZ0dXXB6XSipaUFjY2NCbczGo2w2+1wOp1ob2+Hy+WC1+vFwoUL4Xa7s3gG+alCo8Z7daHE6n9f6Z71lH0iIiIlyVoy5Pf7YbFYoNPpYLVaw7cbDAY4nU50dHTAYrFM2s5iscDtdsPpdEYtTOp0OgEAa9asyXjsuaanpwcPP/xwWqbWS6RxQ2/5L+G17otp2y8REVGuy1oy1NLSAr/fj40bN066z2w2AwDa2trg8XjCt7vdbrS1tUGn08FgMEzabsOGDfD7/XA4HJkLPAdptVqsW7cOWq02bfv88M0N4X8ffJlT7ImIqHBkLRl66qmnAAA6nS7u/SaTCUAoaZLs2LEj6r5YUteazWZLW5xKUFZWhhUrVqCsrCxt+1xYVwF9fQUAoOPld9K2XyIiolyXlWTI7/eHBztHdnVFklp+IscAdXR0AAiNGZpqG4/HE9WilO+GhoZw5MgRDA0NpXW/piVXAwDc533ovZiemWpERES5LivJkNd7ZUX0RDPAamtrASCc1Hg8nvBjE7UmRc6mKqSB1ENDQzh8+HDak6HGW0LJkCiGBlITEREVgqwkQ5FJS6IWnMgWo9iWnkRTyGO3KRR1dXXYtm0b6urq0rrfW+dXo6aiFADQcYpdZUREVBiykgwJghBOXPbu3Tvt42tqaqJakBJ1rUXq6+tLMTqSFBepwgOp//BaL4bHWI2aiIjyX9YGUG/fvh1AqDsrXpeWy+UK/zs2+ZmquKL02OkKMHZ3d+PkyZNRf2fOnEkq9lzT29uLRx99FL296a8WbbrcVXZpbByHz7AaNRER5T91tg7U3NyMvXv3wu12h4sorly5Eh6PB3a7PdzNJY0PikyIIsccxZpuYLZk586d+NrXvjarc8gV5eXlWL16NcrLy9O+7w/cWIdSdRFGgxPoePkdrLmcHBEREeWrrFagdrlcsNlsMBgMaGpqgsViwd69e2G328OPaWpqAgCsXLkyfFsyy27o9fop79+6dSs6Ozuj/p5++umUzkNu5eXlWLVqVUaSoQqNGqv1ocHsHS93Y2KC1aiJiCi/Za1lSNLc3Izm5uao29xud7hlSCrAKI0z8vv9CVuGIgdNJ6pFJGloaEBDQ8OUj1GK4eFhnD59GosXL05rrSGJacnVeOZ0D3oCIzjxVj/efb2Q9mMQERHlipxYtV4qrmi1WqOm0W/YsAEA0NXVFXc7KRkSBCHh9Pt8FAgEsH//fgQCgYzsf83NV7rGOKuMiIjynezJkMPhQFtbGwRBmFRJWuoyk4ovxmpvbwdwZXB2oaivr8dDDz2E+vr6jOz/mqoyLJ9XBYDVqImIKP/Jmgy53W40NTVBEAQcPHhw0iBog8EAk8kU1Y0WyeFwQBCEqIVfKT2kWWWvvB3AG970FnckIiLKJbIlQw6HA0ajETqdDi6XK+5CrEBodXqDwQCLxRI1kFpqNYqXROU7r9eLnTt3TjnLbrZMEbPIfsuuMiIiymNZT4Y6OjrQ2NiIpqYmNDc3o6ura8rxPoIgwOVyYePGjbBYLLBYLOEFWs+ePZswicpnGo0GS5YsgUajydgxbpmrxbzqOQCA35x8O2PHISIikptKFMWszJ3u6OiAzWaDTqdDY2NjeNaYnE6ePIlly5ahs7MTS5culTucnPNv+0/hO8+ehUoFvPCgCfXazCVfREREcsna1HqTyTTt9HdKzsjICM6dO4cFCxZktHXoY8uuwXeePQtRDA2k3nzb/Iwdi4iISC6yzyajmevv78eePXvQ39+f0eMY5leHW4N+3cmuMiIiyk9MhhSovr4eDz74YMam1kuKilT4yJLQQOo/dvWi/9JYRo9HREQkByZDCqRSqVBSUgKVSpXxY61dNhcAMDYu4n9f4awyIiLKP0yGFMjn8+GJJ56Az+fL+LHeq6tB1ZwSAOwqIyKi/MRkSIHUajXmzp0LtTrz499LiovCNYd+92oPhkaDGT8mERFRNjEZUiCtVou1a9dCq9Vm5XgfW3YNAGB4bAK/f7UnK8ckIiLKFiZDCjQ6Oorz589jdHQ0K8f7wI11KC8tBsCuMiIiyj9MhhTI7/dj9+7dUcuTZFJZSTE+dHMDAODgy90YCY5n5bhERETZwGRIgWpra/HAAw+gtrY2a8f82NJQV1lgJIg/dvVl7bhERESZxmRIgYqLi6HValFcXJy1Y37o5gaUqkMvl9+wq4yIiPIIkyEF8vv9ePLJJ7PWTQYAV2nU+OCNdQBCq9iPT2RlSTsiIqKMYzKkQEVFRSgrK0NRUXafvo9e7irzDo7i+bPsKiMiovzAZEiBKisrsX79elRWVmb1uI1Lroa6KFT1+pcnLmT12ERERJnCZEiBgsEguru7EQxmtwCiUF6K91/uKvt159sIjk9k9fhERESZwGRIgbxeL3bt2gWv15v1Y69bfi0AoG9wFM952FVGRETKx2RIgWpqanD//fejpqYm68f+yNKrUVocetnsP86uMiIiUj4mQwqkVqvR0NCQlbXJYlWWleCDN9UDAH598m2MBtlVRkREysZkSIEGBgawb98+DAwMyHL8T66YCwDovzSGw2d6ZYmBiIgoXZgMKdDExASGh4cxMSFPq8yaW66G5nIBxl+c+LMsMRAREaULkyEFEgQBmzdvhiAIshz/Ko0aH768Vln7yXe4VhkRESkakyEFGh8fRyAQwPi4fEnIJ5aHusoCI0H8/lV2lRERkXIxGVKgvr4+PPbYY+jrk29q+4dvbsCcktDaaPvZVUZERArGZEiBBEHAli1bZOsmA4DyUjXW3BLqKus49Q6Gx9hVRkREysRkSIFKS0sxf/58lJaWyhqHVIBxcHQcz7zSLWssREREqWIypECBQAAHDhxAIBCQNY47FtfjKk2o1tF+rlVGREQKxWRIgYLBIC5cuJD1tclilZUUo3HJ1QCAg6+8g8EReeMhIiJKBZMhBaqursa9996L6upquUPBusuzyobHJvDbU2/LHA0REdHMMRlSIFEUMTY2BlEU5Q4FH7ypHtXlJQCAp1/krDIiIlIeJkMK1NPTg0ceeQQ9PT1yh4KS4qLwQOo/vNaDnsCIzBERERHNDJMhBaqqqsKmTZtQVVUldygAgDtvvQ4AMCGy5hARESkPkyEF0mg0WLx4MTQajdyhAAAM8wXMrykHADz94lsyR0NERDQzTIYUaHBwEIcOHcLg4KDcoQAAVCoV7nx3qKvs+Jv98PRclDkiIiKi5DEZUqCRkRGcOnUKIyO5Mz7nU5e7ygDg6ZfYVUZERMrBZEiBampqsHXrVtTU1MgdSpi+/iq867rQGKafv/RWTsx0IyIiSgaTIUobaSD1631DePENv7zBEBERJYnJkAL19PTg4Ycfzomp9ZE+uWIuilShf/+cA6mJiEghmAwpkFarxbp166DVauUOJUqDtgyrF9UBAH5x4gLGxidkjoiIiGh6TIYUqKysDCtWrEBZWZncoUxy57tDXWXewVE8+1qvzNEQERFNj8mQAg0NDeHIkSMYGhqSO5RJPrrsGpSVhF5W+9hVRkRECsBkSIGGhoZw+PDhnEyGrtKo0bjkGgDAb0+9jYHhMZkjIiIimhqTIQWqq6vDtm3bUFdXJ3cocd1lCHWVDY9N4FcnLsgcDRER0dSYDFHafeDGelxdGVoqpM31pszREBERTY3JkAL19vbi0UcfRW9vbg5QLi5SYf2t8wAAx1734WxvbiwbQkREFA+TIQUqLy/H6tWrUV5eLncoCZmN88L//ilbh4iIKIcxGVKg8vJyrFq1KqeToUUNV+HW+QIA4KfuNzE+weU5iIgoNzEZUqDh4WEcP34cw8PDcocyJal16EL/MJ7r6pM5GiIioviYDClQIBDA/v37EQgE5A5lSuuWX4tSdegl1uZ6Q+ZoiIiI4mMypED19fV46KGHUF9fL3coU6qaU4KPLg3VHPr1SdYcIiKi3MRkiDJK6ipjzSEiIspVTIYUyOv1YufOnfB6vXKHMq33L6pjzSEiIsppTIYUSKPRYMmSJdBoNHKHMq3iIhU+bbhSc8jTc1HmiIiIiKIxGVKgiooK3HHHHaioqJA7lKTcZbhSc4itQ0RElGuYDCnQyMgITp8+jZGREblDSUpszaHg+IS8AREREUVgMqRA/f392LNnD/r7++UOJWmb3nM9AOCdgRE8c7pH5miIiIiuYDKkQPX19XjwwQdzfmp9pHXLr0VFaTEAYM8L52WOhoiI6AomQwqkUqlQUlIClUoldyhJq9Co8Zfvvg4A8Mzpbrzdn9vVs4mIqHAwGVIgn8+HJ554Aj6fT+5QZmTzbaGusgkRcB5jRWoiIsoNTIYUSK1WY+7cuVCr1XKHMiPvuq4Kt8ytBADsPfYGJrh4KxER5QAmQwqk1Wqxdu1aaLVauUOZEZVKFW4detN3Cc+e6ZU5IiIiIiZDijQ6Oorz589jdHRU7lBm7FPvvg5lJaGX3Z6jHEhNRETyYzKkQH6/H7t374bf75c7lBmrmlOCj79rLgCg/dQ76L2ojFpJRESUv5gMKVBtbS0eeOAB1NbWyh1KSja9Zz4AYGxcxM/crEhNRETyYjKkQMXFxdBqtSguLpY7lJS8Z0E19PWhpUT2HH0DosiB1EREJB8mQwrk9/vx5JNPKrKbDAgNpJZahzw9g3jhrFfmiIiIqJAxGVKgoqIilJWVoahIuU/fpw3XoaQ4VDTyx89zIDUREclHud+mBayyshLr169HZWWl3KGkrPYqDdYuCw2kPtB5gQOpiYhINkyGFCgYDKK7uxvBYFDuUGbls++7AUBoIPXeo6xITURE8mAypEBerxe7du2C16vssTYrb6jGzdeECkf+5PnzGGdFaiIikgGTIQWqqanB/fffj5qaGrlDmRWVSoW7V4Vah97yX8Izr3TLHBERERUiJkMKpFar0dDQoLi1yeJZf+t1qCgNlQj40fOvyxwNEREVIiZDCjQwMIB9+/ZhYGBA7lBm7SqNGp82zAMA/O7VHrzeNyhzREREVGiYDCnQxMQEhoeHMTExIXcoaXHP5a4yUQyNHSIiIsomJkMKJAgCNm/eDEEQ5A4lLRZfo8VtC0Ljn/YeewPDY+MyR0RERIWEyZACjY+PIxAIYHw8f5KGey5Ps/cPjeGXJy7IHA0RERWSrCZDfr8fTU1NaGxsRGNjI4xGIywWC9xu95Tbtba2Rm3T1NSk2KUo0qGvrw+PPfYY+vr65A4lbT629BrUXVUKgAOpiYgou7KWDLndblRXV0Ov16O9vR3t7e1wuVzQ6XQwGo1oa2uLu53RaITdbofT6Qxv4/V6sXDhwmmTqHwlCAK2bNmSN91kAFCqLgqvV/bieT863+qXOSIiIioUKjFLS4ZXV1dDp9PB5XJNuk+v18Pj8UxavdxisaCtrQ0ulwsGg2HS/gDA5/OlHNPJkyexbNkydHZ2YunSpSnvh9LjLf8lfMD2v5gQgY0rr4fNvFzukIiIqABkpWXI7XbD7/dDp9PFvV9KdDweT9Q2bW1t0Ol0kxIhANiwYQP8fj8cDkdmgs5hgUAABw4cQCAQkDuUtLpOmIMP33w1AODnx9+Cf2hU5oiIiKgQZCUZkpKcRN1a8cb/7NixAwBgMpnibtPY2AgAsNlsaYhQWYLBIC5cuKD4tcni+fztCwAAw2MTePIFrldGRESZl5VkKLLlp6WlJeo+v9+Pjo4OmEymqJajjo4OAKExQ9PtM7JFqRBUV1fj3nvvDXcV5pPVi2px09VXAQB++Nw5BMfzo5YSERHlrqwkQzqdDlarFUBoZpiUEPn9fqxZswYGgwFOpzP8eI/HE24tStS1FrkuV6ENpBZFEWNjY5PGWOUDlUqFz9++EADw5/5h/ObkOzJHRERE+S5rs8nsdjvMZjOAUEJkNBphNBqxceNGuFyuqJlRkS09iRYjTfT4RLq7u3Hy5MmovzNnzqR2MjLr6enBI488gp6eHrlDyYj1t14HobwEALD78FmZoyEionyX1TpDTqcz3ELkdrvh8XjiTg+PHEOUzPTxZOrt7Ny5E8uWLYv6u/POO5OMPLdUVVVh06ZNqKqqkjuUjJhTWhyeZn/sdR9OvOmXNyAiIsprWa9A7fV6owZFNzU1oampKeHjpyquKCVKyRRg3Lp1Kzo7O6P+nn766SSjzi0ajQaLFy+GRqORO5SM+av33YDiIhUAYPfhc/IGQ0REeS1ryZDf74der0dNTQ3a29vR1dUVHg/kcDiiEqLI1iCv1zvlPmMfn0hDQwOWLl0a9bdo0aKUzkVug4ODOHToEAYH83eF92uFOfjYsmsAAPtP/BndA8MyR0RERPkqa8nQmjVr4PV6YbfbAYQGRnd1dYXHETkcjvAMspUrV4a3S6bVR6/Xpz/gHDYyMoJTp05hZGRE7lAy6t7VCwAAY+MifsTV7ImIKEOykgy1tbXB7XZjw4YNk+5zOp3hafJSoiQIQri1J1HLUOSg6US1iPJVTU0Ntm7dmnBweb4wzK/G8nmhcVE/ef51jATzZ2FaIiLKHVktupioO2v79u1RjwMQTpy6urqm3Wei6fekbCqVClsutw71XhzFL45zNXsiIkq/rNUZAhJPgZfuj+wek8YQSV1nsdrb2wFcSaQKSU9PDx5++OG8nVof6RPvuhb12tBA8d2Hz+ZlbSUiIpJXVpIhqRuro6Mj7hggKeGRltgAQhWmTSZTeAp+LIfDAUEQwlP1C4lWq8W6deug1WrlDiXjStVFuOe9NwAATv55AM+fTTygnoiIKBVZSYYEQYDT6YTf74fFYolKiNxuN3bs2AGr1RoeTC2RxhPFbiO1Gh08eDCpmWT5pqysDCtWrEBZWZncoWTF3avmo1Qdeql+5w+FtfQKERFlnjpbBzKbzejq6kJLSwuMRiMEQQgPAHY6nXEHQQuCAJfLhdbWVlgsFgiCAL/fD51Oh7NnzxZkIgQAQ0NDOHHiBJYvX47y8nK5w8m4uqs0uMswD0++cB4dL3fjTPdFLGq4Su6wiIgoT6jEAh6EcfLkSSxbtgydnZ1YunSp3OEkrbe3F9///vfxuc99DnV1dXKHkxVdPRex5tHfAQA2ved6/Ptdy2WOiIiI8kXWK1DT7NXV1WHbtm0FkwgBgL7+KphuuRoA8DP3W+gOsAgjERGlB5MhUoymvwjNOhwdn8AP/vi6zNEQEVG+YDKkQL29vXj00UfR29srdyhZtfKGarz7egEA8MMjr2NoNChvQERElBeYDClQeXk5Vq9eXRCDpyOpVCo0fTDUOtR/aQzOY2/KHBEREeUDJkMKVF5ejlWrVhVcMgQAH1l6DebXhM77O896EByfkDkiIiJSOiZDCjQ8PIzjx49jeLjwBhEXF6nwxQ8sBAC84b2E35x8R+aIiIhI6ZgMKVAgEMD+/fsRCATkDkUWFuP1qC4vAQA4ft/FJTqIiGhWmAwpUH19PR566CHU19fLHYos5pQW47OrQkt0HH+zHy9wiQ4iIpoFJkOkSJ9934LwEh3233OJDiIiSh2TIQXyer3YuXMnvN7CbRGp12pgNs4DAPzvK9049ecBmSMiIiKlYjKkQBqNBkuWLIFGo5E7FFn99Qf1KFKF/r3rd13yBkNERIrFZEiBKioqcMcdd6CiokLuUGQ1v7Ycn1xxLQDglyf+jHO9gzJHRERESsRkSIFGRkZw+vRpjIyMyB2K7O6/Qw8AmBAB++/ZOkRERDPHZEiB+vv7sWfPHvT398sdiuxuvqYyvIBrm+tNvN1feLWXiIhodpgMKVB9fT0efPDBgp1aH2vrh0KtQ2PjIh7/A2eWERHRzDAZUiCVSoWSkhKoVCq5Q8kJhvnVuF1fCwD4yfPn4RsclTkiIiJSEiZDCuTz+fDEE0/A5/PJHUrO2HrHIgDApbFx7P7jOXmDISIiRWEypEBqtRpz586FWq2WO5ScsXpRLVbMqwIAfO/wWVwcCcocERERKQWTIQXSarVYu3YttFqt3KHkDJVKha0fCrUODQwH8ZPnX5c5IiIiUgomQwo0OjqK8+fPY3SUY2MiNd5yNW5suAoA8PgfzmJ4bFzmiIiISAmYDCmQ3+/H7t274ff75Q4lpxQVqcIzy3oCI9jzwnmZIyIiIiVgMqRAtbW1eOCBB1BbWyt3KDnnk8uvxYLacgChJTrYOkRERNNhMqRAxcXF0Gq1KC4uljuUnKMuLsLfXB479M7ACJ469obMERERUa5jMqRAfr8fTz75JLvJElh/63WYX3O5dehQF0aCbB0iIqLEmAwpUFFREcrKylBUxKcvHnVxEf72cuvQhf5hPHXsTZkjIiKiXMZvUwWqrKzE+vXrUVlZKXcoOWu94TpcXzMHALDrmTNsHSIiooSYDClQMBhEd3c3gkEWFkykpLgIf3O5KvWf+4fR5mLrEBERxcdkSIG8Xi927doFr9crdyg57dOGebhOCLUO7XymC6PBCZkjIiKiXMRkSIFqampw//33o6amRu5Qclqp+srMsrf8l9g6REREcTEZUiC1Wo2GhgauTZYEs/FK69C3njnD1iEiIpqEyZACDQwMYN++fRgYGJA7lJxXqi7C/XeEqlK/5b+En7rZOkRERNGYDCnQxMQEhoeHMTHBVo5kWFbOw7VVZQCA/zr4GmeWERFRFCZDCiQIAjZv3gxBEOQORRE06mL8nzU3AgjNLHvyea5ZRkREVzAZUqDx8XEEAgGMj7OFI1lm4zzccHnNsv9+pgtDoyxLQEREIUyGFKivrw+PPfYY+vr65A5FMUqKi/Al000AgN6LI/jBc6/LHBEREeUKJkMKJAgCtmzZwm6yGfrkimtxY8NVAIBv/64LA8NjMkdERES5gMmQApWWlmL+/PkoLS2VOxRFKS5SYdtHQq1D/qExfPcPZ2WOiIiIcgGTIQUKBAI4cOAAAoGA3KEozkeXXoNl14XWdPvus2fhGxyVOSIiIpIbkyEFCgaDuHDhAtcmS4FKpcK2jywGAFwcCeLbv++SOSIiIpIbkyEFqq6uxr333ovq6mq5Q1GkO26qx8obQtfu+388h+7AsMwRERGRnJgMKZAoihgbG4MoinKHokgqlQr/+NFQ69Dw2AR2PsPWISKiQsZkSIF6enrwyCOPoKenR+5QFGuVrhbvX1QHAPjJ8+fxlv+SzBEREZFcmAwpUFVVFTZt2oSqqiq5Q1E0qXVodHwC/9H+qszREBGRXJgMKZBGo8HixYuh0WjkDkXR3n29gI8suRoA8FP3mzj9NmfnEREVIiZDCjQ4OIhDhw5hcHBQ7lAUr/lji1GkAiZEoPXXr8gdDhERyYDJkAKNjIzg1KlTGBkZkTsUxVvUoMXG91wPADj4Sjee93CJEyKiQsNkSIFqamqwdetW1NTUyB1KXvgH000oKwm9FXYceIWz9IiICgyTISp4V1eW4QvvXwgAeOkNP37d+bbMERERUTYxGVKgnp4ePPzww5xan0ZNf6FHdXkJAKD1N6cxNj4hc0RERJQtTIYUSKvVYt26ddBqtXKHkjcqy0rwtx++EQBwtncQe4++IXNERESULUyGFKisrAwrVqxAWVmZ3KHklXtWzce86jkAgP/oeA2DI1z7jYioEDAZUqChoSEcOXIEQ0NDcoeSVzTqYvzj5UVcey+O4LvPnpU5IiIiygYmQwo0NDSEw4cPMxnKgL9ccS2WzK0EANh/14XeiyxfQESU75gMKVBdXR22bduGuro6uUPJO0VFKnxl7c0AgMHRcfy/jtdkjoiIiDKNyRBRjA/cWIcP3Hh5EdcXzuO1d7hMBxFRPmMypEC9vb149NFH0dvbK3coeUmlUuGhT9yCIhUwPiHikV+9LHdIRESUQUyGFKi8vByrV69GeXm53KHkrZuvqQwv0/HM6R78/lXWdCIiyldMhhSovLwcq1atYjKUYQ80LkZFaTEA4N9+eQpBFmIkIspLTIYUaHh4GMePH8fw8LDcoeS1eq0GWz+0CADw6jsXsfcYCzESEeUjJkMKFAgEsH//fgQCHNibaV94/0JcJ4QKMT7221cRGB6TOSIiIko3JkMKVF9fj4ceegj19fVyh5L3ykqK0XJ5qn3f4Ch2HuqSOSIiIko3JkNE0/jk8rm4db4AAPjus2fxhpfFLomI8gmTIQXyer3YuXMnvF6v3KEUBJVKhX9atwQAMBqcgO3Xr8gcERERpROTIQXSaDRYsmQJNBqN3KEUDMP8anxyxbUAgP0nLsD1OhNRIqJ8wWRIgSoqKnDHHXegoqJC7lAKSsvHFqNUHXrL/Mv/nMLEhChzRERElA5MhhRoZGQEp0+fxsgIFxHNpnnV5Wj6oA4A8Ke3+vEUp9oTEeUFJkMK1N/fjz179qC/v1/uUArO/XfocW1VGQCg9Ten0X+JU+2JiJSOyZAC1dfX48EHH+TUehmUl6rx4CduAQB4B0fxzfZXZY6IiIhmi8mQAqlUKpSUlEClUskdSkH6xLvmYpWuBgDwwyOv4/TbLH5JRKRkTIYUyOfz4YknnoDP55M7lIKkUqnwL3+5FMVFKoxPiPiX/zkJUeRgaiIipWIypEBqtRpz586FWq2WO5SCdfM1lfjsqhsAAM95+nCg822ZIyIiolQxGVIgrVaLtWvXQqvVyh1KQfuS6SbUVJQCAB7+5cu4NDouc0RERJQKJkMKNDo6ivPnz2N0dFTuUApaVXkJvvzRxQCAt/yXsOt3XLeMiEiJspYM+f1+qFSqaf/0en3c7VtbW9HY2IjGxkYYjUY0NTXB7/dnK/yc4vf7sXv37oI9/1yyYeX1WHZdJQDg27/r4rplREQKlLVkaMeOHUk9zmw2T7rNaDTCbrfD6XSivb0dLpcLXq8XCxcuhNvtTneoOa+2thYPPPAAamtr5Q6l4BUXqfC1v1wKILRu2df3n5I5IiIimqmsJUNtbW1wOp0QRTHun9PpBABs3LgxajuLxQK32w2n0wlBEMK3S49fs2ZNtk4hZxQXF0Or1aK4uFjuUAiA8YYafPrW6wAAvz31Dg6d7pY5IiIimomsJEMdHR1oaWmJ2+ojsdvtEAQBBoMhfJvb7UZbWxt0Ol3U7ZINGzbA7/fD4XBkJO5c5ff78eSTT7KbLId8Ze3N0GpCs/u++vOTGB7jYGoiIqXISjJkMplgtVqnfExHRwc2bNgQdZvUtWYymeJu09jYCACw2WxpiFI5ioqKUFZWhqIijn/PFQ2VZdj2kZsAAOe9Q9j5zBmZIyIiomTlxLdpW1sbgFCXWKSOjg4AoTFD8UitRR6PBx6PJ4MR5pbKykqsX78elZWVcodCEe5ZdQOWXisNpvbA03NR5oiIiCgZOZEM7d27F0B0C5DH4wl3A+l0urjb1dTUhP9dSAOpg8Eguru7EQwG5Q6FIqiLi/Dw+ndBpQJGxyfwTz/vZGVqIiIFyIlkqK2tbdJ4osiWnsikJ1LkgOrpWoa6u7tx8uTJqL8zZ5TZleH1erFr1y54vV65Q6EY775ewGdumw8AOHymD784cUHmiIiIaDqyr+cgdZHFziKLHBwcmfQk0tfXN+X9O3fuxNe+9rUZx5eLampqcP/99ydMEklezR+9Gb85+TZ6L47i6/tP4Y7F9agsK5E7LCIiSkD2liGpi2yqmWZTzZqSEqXpZlZt3boVnZ2dUX9PP/30DKPNDWq1Gg0NDVybLEdVlZfgwY/fAgDoCYzgsd++KnNEREQ0Fdm/TeN1kQHRrUFTdQdJSdB0rUcNDQ1oaGhIJcScMzAwgIMHD2LNmjUcRJ2j1t96HZ469gaOeLz4wXPnYDbOw7LrquQOi4iI4pC1ZUiaLRbbRQYAK1euDP87mXo6iZbxyEcTExMYHh7GxMSE3KFQAiqVCv925zKoi1SYEIGH9v0J4xMcTE1ElItkTYbsdjuA+HWEBEEIt/YkahmKHDSdqBZRPhIEAZs3b05qLBXJZ1GDFvd9MDQT8vib/fjJC+dljoiIiOKRNRlqa2uDyWRK+KUuFWHs6oq/GriUDAmCkHD6fT4aHx9HIBDA+DirHOe6v/vwjbhOmAMAaD3wCt4ZGJY5IiIiiiVbMiR1kcUWWozU1NQU9dhY7e3tAIDt27enObrc1tfXh8cee2zaGXQkvzmlxfi3O5cBAAIjQXz1550yR0RERLFkS4akhVZjl+CIZDAYYDKZ4Ha749YRcjgcEARh2qU+8o0gCNiyZQu7yRTiQzc34JMrrgUA/ObkO/h159syR0RERJFkS4aeeuopGAyGab/QnU4nDAYDLBZL1EBqqdXo4MGDBZcUlJaWYv78+SgtLZU7FErSV9ctQdWcUK2hr/68EwPDYzJHREREElmSoY6ODvj9/rizyGIJggCXy4WNGzfCYrHAYrGEF2g9e/Zs3NXs810gEMCBAwcQCATkDoWSVK/V4KFPhGoPdQdGYDvwiswRERGRRCUW8OJJJ0+exLJly9DZ2YmlS5fKHU7SfD4f9u3bh/Xr16O6ulrucChJoiji7u88jz92hcZ6Of/6fXjPAlYRJyKSm+wVqGnmqqurce+99zIRUhiVSoVH1r8LGnXobfeVn57ASJAzAomI5MZkSIFEUcTY2BhXRFegBXUV+HvTjQCArp5BfOuZ+GUjiIgoe5gMKVBPTw8eeeQR9PT0yB0KpeC+D+hwy9zQMiq7Dp3Bq+9w7BcRkZyYDClQVVUVNm3ahKoqrnWlRCXFRbDd9S4UqYCxcRHbf/YnTHCpDiIi2TAZUiCNRoPFixdDo9HIHQqlaPk8AVtWLwQAuF734QfPnZM3ICKiAsZkSIEGBwdx6NAhDA4Oyh0KzcK2j9yEedWhpTpsvz6N831DMkdERFSYmAwp0MjICE6dOoWRkRG5Q6FZKC9Vo/Wu5QCAS2PjaP7pcXaXERHJgMmQAtXU1GDr1q2oqWGNGqW7fVEd7n7vfADAEY8XP+bK9kREWcdkiEhm2z9+S3hl+x2/ehlveNldRkSUTUyGFKinpwcPP/wwp9bnias0avz7Xe8CAAyNjuMrPzvBGlJERFnEZEiBtFot1q1bB61WK3colCYfuLEem2+7HgBw+EwfnnzhDZkjIiIqHEyGFKisrAwrVqxAWVmZ3KFQGj348VtwbVXoOX3kVy/jLf8lmSMiIioMTIYUaGhoCEeOHMHQEMeW5BNtWQl2XJ5ddnEkiK/8lN1lRETZwGRIgYaGhnD48GEmQ3noL26qx4aV8wAAf3itF08dY3cZEVGmMRlSoLq6Omzbtg11dXVyh0IZ8NAnluCaylB32b/tfxl/ZncZEVFGMRkiyjFVc0qw49Oh2WWBkSBa2F1GRJRRTIYUqLe3F48++ih6e3vlDoUy5EM3N8BsvNJd9qMjr8scERFR/mIypEDl5eVYvXo1ysvL5Q6FMuirn1wSLsb48K9extlerkVHRJQJTIYUqLy8HKtWrWIylOcqy0rwDcsKAMDw2AS+tPclBMcnZI6KiCj/MBlSoOHhYRw/fhzDw8Nyh0IZ9j59Lb7w/oUAgJfe8OPbv+uSOSIiovzDZEiBAoEA9u/fj0AgIHcolAVf/uhiLGq4CgDwHx2vofOtfpkjIiLKL0yGFKi+vh4PPfQQ6uvr5Q6FsqCspBjf3PBuqItUCE6I+NLelzA8Ni53WEREeYPJEJECvGteFf5uzY0AgNe6L+LR356WOSIiovzBZEiBvF4vdu7cCa/XK3colEVb79BjxfUCAOA7z57FEU+fvAEREeUJJkMKpNFosGTJEmg0GrlDoSxSFxfhsQ0rUFZSBFEEtj11HIHhMbnDIiJSPCZDClRRUYE77rgDFRUVcodCWaavvwrb194CAHjLfwlf+8UpmSMiIlI+JkMKNDIygtOnT2NkZETuUEgGn111Az5wY2hdujbXm/jliQsyR0REpGxMhhSov78fe/bsQX8/p1gXoqIiFf6veQWE8hIAwPafncBbXMyViChlTIYUqL6+Hg8++CCn1hewa6rKYLtrOQBgYDiIL+19CeMTXMyViCgVTIYUSKVSoaSkBCqVSu5QSEYfXXoNNt82HwDwwlkvq1MTEaWIyZAC+Xw+PPHEE/D5fHKHQjL7p3W3QFcfGkj/zfZX8dIbfnkDIiJSICZDCqRWqzF37lyo1Wq5QyGZlZeq8Z+bbkVJcag69d/veREXR4Jyh0VEpChMhhRIq9Vi7dq10Gq1codCOWDZdVX48kcXAwBe7xvCv/zPSZkjIiJSFiZDCjQ6Oorz589jdHRU7lAoR3zx/Tq8f9GV6fb7T/xZ5oiIiJSDyZAC+f1+7N69G36/X+5QKEcUFanw6IbI6fZ/4nR7IqIkMRlSoNraWjzwwAOora2VOxTKIVdXXpluHxgO4kt7XkJwfELmqIiIch+TIQUqLi6GVqtFcXGx3KFQjomabn/Oi/88+JrMERER5T4mQwrk9/vx5JNPspuM4vrquiW46eqrAAD/9cwZ/PFMr8wRERHlNiZDClRUVISysjIUFfHpo8nmlBbjvz9jCK9u//d7X0JPgOvYERElwm9TBaqsrMT69etRWVkpdyiUo266Wot//ctlAICewAgeeOolTHC5DiKiuJgMKVAwGER3dzeCQRbXo8QsK+fhzndfCwD4w2u92MXlOoiI4mIypEBerxe7du2C1+uVOxTKYSqVCv+2/l1YWBdaruOx9ldx7BxfM0REsZgMKVBNTQ3uv/9+1NTUyB0K5birNGr892duRWlxEcYnRPzdky/CN8hinUREkZgMKZBarUZDQwPXJqOkLL22Cv/fulsAAH/uH8aX245DFDl+iIhIwmRIgQYGBrBv3z4MDAzIHQopxGdX3YCPLb0GANDxcjeeOHxO3oCIiHIIkyEFmpiYwPDwMCYmWF2YkqNSqWAzL8e86jkAgH8/8DJOvOmXNygiohzBZEiBBEHA5s2bIQiC3KGQglTNKcF/bb4V6iIVxsZFbP2xG/1DY3KHRUQkOyZDCjQ+Po5AIIDx8XG5QyGFuXV+NVo+djMA4E3fJdYfIiICkyFF6uvrw2OPPYa+vj65QyEF+uIHFuIjS64GABx8pRvf/j3rDxFRYWMypECCIGDLli3sJqOUqFQq/F/LCtxQWw4A+MZvTuOPXVy/jIgKF5MhBSotLcX8+fNRWloqdyikUFVzSrDrbiM06iJMiMDfPfki3hkYljssIiJZMBlSoEAggAMHDiAQCMgdCinYkmsr8fU7Q+uX9V4cxf/5yYsYG+cMRSIqPEyGFCgYDOLChQtcm4xmbcPK67Fh5TwAwAvnvPi/vzktc0RERNnHZEiBqqurce+996K6ulruUCgP/OunlmHJ3EoAgOP3Hvy6822ZIyIiyi4mQwokiiLGxsa4pAKlRVlJMXbdY4C2LLS8y5edx3Gud1DmqIiIsofJkAL19PTgkUceQU9Pj9yhUJ64obYC37CsAAAERoK4/8duDI+xjhURFQYmQwpUVVWFTZs2oaqqSu5QKI98dOk1aPqgDgDw8oUBPLSvk62PRFQQmAwpkEajweLFi6HRaOQOhfLMlz+6GLctrAEA/NT9Jn545HWZIyIiyjwmQwo0ODiIQ4cOYXCQ4zoovdTFRfjWZwy4prIMAPCvvziFF856ZY6KiCizmAwp0MjICE6dOoWRkRG5Q6E8VK/VYNc9BpQWFyE4IWLrj114u58FGYkofzEZUqCamhps3boVNTU1codCeerW+dX4+p1LAYQKMv71j1wYCXJANRHlJyZDRBTXxvfMx2feOx8A8NIbfvzL/5yUOSIiosxgMqRAPT09ePjhhzm1njLunz+5BIb5AgDgyRfewE+ePy9vQEREGcBkSIG0Wi3WrVsHrVYrdyiU5zTqYuy6x4h6bWjm4j//Tydcr/tkjoqIKL2YDClQWVkZVqxYgbKyMrlDoQJwdWUZdt1tgLpIhbFxEff/yIVurnBPRHmEyZACDQ0N4ciRIxgaGpI7FCoQKxfU4J//MjSgujswgq0/dmM0yBXuiSg/MBlSoKGhIRw+fJjJEGXVPe+dH17h/tjrPnx9/ymZIyIiSg8mQwpUV1eHbdu2oa6uTu5QqICoVCr866eWYcW80DIwPzzyOgdUE1FeYDJEREkLrXBvRN1VoQHVX/15J454+mSOiohodpgMKVBvby8effRR9Pb2yh0KFaBrhTmwf9YYrlB9/49ceMPLLlsiUi4mQwpUXl6O1atXo7y8XO5QqEAZb6jGI59+FwDANzSGL37/GC6OBGWOiogoNUyGFKi8vByrVq1iMkSyMhvn4b4PLAQAnH4ngC/tfQkTE6LMURERzVxOJENutxutra1oampCa2ur3OHkvOHhYRw/fhzDw6z1QvL6ytpbcMfiegBA+6l38Gj7aZkjIiKaOVmTIYfDAb1eD4vFAkEQYLPZ0NzcHPexra2taGxsRGNjI4xGI5qamuD3+7MbcI4IBALYv38/AoGA3KFQgSsuUuE/N98KfX0FAOBbz3Th5y+9JXNUREQzoxJFMevt2n6/H2vWrIHb7YbVaoXdbp/y8UajEX6/Hy6XC4IgAAAsFgs6Ojpw8OBBGAyGlOI4efIkli1bhs7OTixdujSlfRARcLZ3EHd+6zD6L41Boy6C86/fh+XzBLnDIiJKStZbhvx+P4xGI9xuN+x2+7SJkMVigdvthtPpDCdCAOB0OgEAa9asyWS4RJSEhXUV+NZnDCguUmEkOAHrD7hkBxEpR9aTIaPRCI/HA7PZDKvVOuVj3W432traoNPp4rb+bNiwAX6/Hw6HI1Ph5iSv14udO3fC6/XKHQpR2PtvrMM/feIWAMDbA8O474cuDI+NyxwVEdH0spoMNTU1wePxQBCEcMvOVHbs2AEAMJlMce9vbGwEANhstvQFqQAajQZLliyBRqOROxSiKJ+7fQE2ved6AMDxN/z4ctsJyNATT0Q0I1lLhiJbcDZs2JDUNh0dHQBCrUnxSK1FHo8HHo8nDVEqQ0VFBe644w5UVFTIHQpRFGnJjtsW1gAAfnH8z/hmx2syR0VENLWsJUORXVnSbDC9Xg+VSgWj0YiWlpaox3s8nvBsMZ1OF3efNTU14X+73e70B52jRkZGcPr0aYyMjMgdCtEkpeoi2O8xYkFtqA7Wfx58DftefFPmqIiIEstaMrR3797wv51OJxobG2G322Gz2eDxeNDa2gq9Xh9OgCJbeiKTnkiRA6oLqWWov78fe/bsQX9/v9yhEMVVXVGKJz7/HlTNKQEAtLT9CS+c5Rg3IspN6mwdSGq5MZlMaG9vD99uMplgNpuh1+vh8XjQ0tICu90eVUMoMulJpK9v6sUiu7u70dPTE3XbmTNnkj+BHFJfX48HH3wQanXWnj6iGdPVXwX7Z4347Hefx+j4BJp+eAz7tq7Ggjp27xJRbsn6bDJp0HMknU4XLrbocDgmFVOcqriilChNV4Bx586dWLZsWdTfnXfeOYPIc4dKpUJJSQlUKpXcoRBNaZWuFo+sv7KG2b3fOwr/0KjMURERRct6MpSolWfjxo3hf0szziRTTSGXkqDpWo+2bt2Kzs7OqL+nn346yahzi8/nwxNPPAGfzyd3KETTsqy8Hn/zIT0AwNM7iL/+kQujwQmZoyIiuiJr/SyCIMDv9ydswYkcJH3s2LGoGWfJLLuh1+unvL+hoQENDQ1JxZrr1Go15s6dy24yUoxtjYtxrncIv/zTBRzxePHQvj+h1bycrZtElBOy1jIkJTuJxvZEtuzodDoIghC+LVHLUOSg6US1iPKRVqvF2rVrodVq5Q6FKClFRSo8umEF3n29AABwut7Ert91yRsUEdFlWUuGVq5cCSC5KfBS4iS1DnV1xf/QlJIhQRASTr/PR6Ojozh//jxGRzn2gpSjrKQYj//VSlwnzAEAtP76NH71pwsyR0VElMVkqKmpCcCVQoqxpMRGp9OFE5vptpFmpW3fvj2tseY6v9+P3bt3J9V9SJRL6rUaPPH59+AqTaiL90t7X4L7PMe+EZG8spYMGQwGmM1mAEBra+uk+6UFWyMXbjUYDDCZTHC73XHrCDkcDgiCMO0aZ/mmtrYWDzzwAGpra+UOhWjGFl+jxbfuvrKo6xe/fwznegflDouIClhWZ5M9/vjjMJlMaGlpQVtbW/h2t9sNh8OB5ubmSWN/nE4nDAYDLBZLVEuI1Gp08ODBpOoQ5ZPi4mJotVoUFxfLHQpRSv7ipnp8/VPLAADewVF8fvcL6LvIiupEJA+VKMMqiq2trWhvb4fH4wkPlm5qappyELS0jTQrTafTwWazzSoROnnyJJYtW4bOzk4sXbo05f1km9/vx4EDB7B27dqCSwQpv/zf37yCbz0TGhP47usFPHnfKswpZZJPRNklSzKUK5SaDA0MDODgwYNYs2YNKisr5Q6HKGWiKGLbU8fxsxffAgA0Lrka377HiOIiTrknouzJetFFmr3KykqsX7+eiRApnkqlwr/ftRyrF4XGv7Wfegdf+8VJFPBvNCKSAZMhBQoGg+ju7kYwGJQ7FKJZK1UXYdc9Rtx8Tahu1g+eex2O3xfOwstEJD8mQwrk9Xqxa9euKZcpIVKSyrIS7N7yHlxTWQYA2HHgFfzP8T/LHBURFQomQwpUU1OD+++/HzU1NXKHQpQ2c6vm4Hv3vgfayzWI/vGp4zjiiV+xnogonZgMKZBarUZDQwPXJqO8c/M1lbB/1oiSYhVGxydg/cExvPZOQO6wiCjPMRlSoIGBAezbtw8DAwNyh0KUdrcvqkOreTkAYGA4iM/vPop3BoZljoqI8hmTIQWamJjA8PAwJiYm5A6FKCPW3zoPX/7oYgDAW/5L+NwTL6D/0pjMURFRvmIypECCIGDz5s0suEh5besdetz93vkAgFfeDuCL3z+K4bFxmaMionzEZEiBxsfHEQgEMD7OLwbKXyqVCv/6qWVYu+waAMDRcz787U9eRHCcLaJElF5MhhSor68Pjz32GPr6ONOG8ltxkQrf3PhurNKFZk52vPwOHtrXyaKMRJRWTIYUSBAEbNmyhd1kVBDKSorx+F+txJK5oYrre4+9gW/89rTMURFRPmEypEClpaWYP38+SktL5Q6FKCu0ZSX43r3vwfyacgDAt57pwu7DZ2WOiojyBZMhBQoEAjhw4AACAdZfocLRoC3DD79wG+quCv0I+Nf9p1ilmojSgsmQAgWDQVy4cIFrk1HBuaG2At/bchuu0qghisC2p17CH17rkTssIlI4JkMKVF1djXvvvRfV1dVyh0KUdcuuq4Ljr4woLS7C2LiIph+6cPwNv9xhEZGCMRlSIFEUMTY2xhk1VLBu19fhPza9GyoVMDQ6ji3fOwpPz0W5wyIihWIypEA9PT145JFH0NPD7gEqXB9/11x8/VPLAADewVF89rsv4EL/JZmjIiIlYjKkQFVVVdi0aROqqqrkDoVIVvesugF/v+ZGAKFlO+75zvPouzgic1REpDRMhhRIo9Fg8eLF0Gg0codCJLt/MN2Iz9++AADQ1TOIz+1+AQPDhbGOWUdHBywWC6qrq6FSqcJ/1dXVaGpqgtvtljvErGhtbUV1dTXa2trkDmWSpqYmNDY2QqVSQa/Xw+FwZD0Gt9uN1tZWGI1G6PX6rB9fCZgMKdDg4CAOHTqEwcFBuUMhkp1KpcJX1y3Bpw3XAQA63xrAF793DJdG83+5GpPJBKfTCZfLFb5NEAT4fD7Y7XYYDAYZo8seu90Ov9+PvXv3yh1KmN/vh16vh8ViQXt7O+x2OzweD5qamuD3+7MWh9vtRkdHB3bs2FEwyXEq1HIHQDM3MjKCU6dOYfny5aioqJA7HCLZFRWp0HrXclwcDuK3p97BC+e8uP/HLjg+uxKl6vz/zafT6eL+Ox38fn/OV7u32+1wOp1oaWmJe78c59DS0gKPxwOTyQQAsFqtssRiMBjCSXGi60NsGVKkmpoabN26FTU1NXKHQpQz1MVF+K/P3Ir3L6oDABw63YMvPfUSxicKa9Zluj8X1qxZk9WWjFSYTCbY7faEiaAc5+BwOCYlPVarFc3NzVmNQ5LrCa3cmAwRUd7QqIth/6wRt84XAAC/PHEBD+37E8tQpKilpUXxXStynIPH48nq8Wj2mAwpUE9PDx5++GFOrSeKo0Kjxvc+fxtuvkYLANhz9A08/MuXmRDNkMPhQGtrq9xhzIpc55DrLWk0GZMhBdJqtVi3bh20Wq3coRDlpKryEvzgC7dhQW1oYdfvPHsW//2/Z2SOSj4ejyc8m6ijowNAaJZTdXV1eOZZpLa2tqjxJdIsJIvFMmm/FosFer0e1dXVaGxsDO9/NseP3K6xsRGNjY3h4+j1+knH8Hg8aGlpiYpvunNoa2uLmoVnNBonzUZrbW0Nz85LZqaaw+GAXq/HmjVrAFwZRC39xbYY+f3+8GyzyPgStWS1tbXBaDTC7XbD4/HAaDSiuro6LTPUko3F7XZHXbfGxsbweTU1NUXNamxqaoo6Z+l6qlSq3GtxFAtYZ2enCEDs7OyUOxQiyoA3vIPiqkc6xBta9os3tOwXdz/rkTukjAEgAhBNJlPU7e3t7aLJZArfb7fbRYPBIFqtVrG5uVkUBEEEIJrN5qjturq6wtv4fL5Jx7Pb7aLJZBK7urpEURRFl8sl6nQ6EYBos9nSdvz29vaEt7lcrqh9x577dOcQeb/dbo97XXU6XcL7Emlvbw/vNxGn0ykKghB1rXw+n2gwGEQAYnNzc/h2u90evrYARKfTKRoMhvBtBoNh2pjsdrsIQNTpdLOKJfb8pOdf0tzcnPA4oiiKJpMp6ji5gsmQApOhwcFB8bnnnhMHBwflDoUo5732TkA0/OtvwwnRU0fPyx1SRiRKCGLvNxgMUYlBoi/uqRIJaZvY251OZ8IvyZkeX/pSjT2GzWYTnU5n1G1SQjTTZGiqbSO3n6npkiFpv7EJoCiGkhApQYxMwnw+X9zn2GaziS6Xa9qYEiVDqcQiimLcxDc2znjXXBCEhM+FnNhNpkBDQ0M4fPgwhoaG5A6FKOctargK37/3Nmg1oUoiLT89gf85/meZo8o+aTbR9u3bo2YWSVO/ASTdddHS0gKTyTRphlLkvux2+6yOL4272bFjR9R+zGbzpFljs6mnJHWldXR0TOrGamtrg9lsTnnfiUjdghs3bpx0nyAI4Wn4kd2Hkdcs8vbm5uZZnX8qsUT+P97zLMUT233X1tYW93WTC5gMKVBdXR22bduGuro6uUMhUoRl11Vh95b3oLy0GBMi8KW9L+HXnW/LHZYi+f3+cCG/yPEwer0eRqMROp0OOp1u1oOIpbE9ra2tUeNSdDrdpC//2tralI9jMpnC+7PZbFH32e32hOOZZkMa85SoFEBkYhIvQU1nLalUY5GSJI/HMymJlf4fmyjt2LED27dvT0/gacZkiIgKwsoFNfjO51ZCoy7C+ISI//OkG8+80i13WIpz7NgxAKEWmq6uroR/sV+EM2UymcLJiZR4Zap6s/QF7XA4wvv3eDzwer1RLVfpkMy0+8hkT7remTCbWARBCLeaRT7XLS0taG5uhk6ng8fjCSdbUoKUq1XRmQwpUG9vLx599FH09vbKHQqRotyur4P9s0aUFhdhbFxE049cOHyG76NUZKOWTnNzM9rb28OtFg6HAwsXLkz7TKTIrjepa8dut4dbPzIlmWuYreK6qcQSmUQCoVahp556Ctu3b5/UjZapVrZ0YTKkQOXl5Vi9ejXKy8vlDoVIce5Y3IBv3W2AukiF0eAEvvD9o3jhrFfusBRDShrcbveUrTTpasExmUzhliZBEOD3+8NT19NJGjskjVFqa2vLyJd3ZHfU0aNHp318JltSZhuLwWCISiJ37NiBDRs2RI01amtrg8fjwVNPPZXx5HI2mAwpUHl5OVatWsVkiChFjUuuxv/bdCuKVMDw2AS27H4B7vM+ucNShMgv0KnWurrvvvtmdZzYRMRqtYYXpPX7/WlvmbJareFkq6mpCYIgpH2dN4nUvZSodlHk+KhMxZCuWKTXgM1mg8PhCHdtRnajNTY2YsOGDWmPPZ2YDCnQ8PAwjh8/juHhYblDIVKsTyyfi0c3rIBKBQyOjuNzT7yAzrf65Q5r1rze1Fu5IreN7BKJTDz8fn94fS2HwzEpIXK73eHxPbM5PjD5C1qn04XH8MT7Yo7dfqpziCey2yeTXTqPP/54OKZ4RSqlriWn05mxGNIVS+RAaqlVSCJdQ6koZi5jMqRAgUAA+/fvRyAQkDsUIkVbf+s87Fj/LgBAYDiIz373ebzy9oDMUc1c5Bd9ohaTyIHB8W6P/Xdky8h9990Hh8OBxsZGAKGkQeoykaoKS1WojUYjbDbbpIHHMz2+dNzYxx87dmzSYqddXV1xt5/qHOKJ3O9sWjKmS7wEQUB7ezsEQZhU5bmjowMOhwN2uz2qWypyP6m0iknXKDZhTCWWWFJCFJvwmEym8Oy/TLdwzZrchY7kpNSii0SUXt//49lwUUbj138rvvZOQO6QktLe3i5ardZwYTzpT6fTiVarVezq6ppUpTmy0nB7e3u4yrC0XWRxPamqtCAIotlsjlttWNpep9PFfUyqx29ubhbNZnP42GazWTQYDJPis1qtUeduNpujqlZPdw6xpGOlwuVyRVXVxuUikzabLe5xfT6faLVaRYPBIBoMBtFsNoeft0hS1e7Ia9jc3JxUsUUppthrFFu4MtlY4unq6hKtVmvc+2w2W9TzkatUoli4qxeePHkSy5YtQ2dnJ5YuXSp3OEQko8d/78HDv3oZAHB1pQZPNb0PN9RWyBwVZZter4fdbk/7lHrKbewmUyCv14udO3fOamwAEUW774M6bGu8CQDwzsAINjuO4Hwfq7wXEqmLiIlQ4WEypEAajQZLliyBRqOROxSivPJ/1tyIv/3QIgDAn/uHsfnxI3jDy4SoUOzYsSOna+FQ5rCbjN1kRBRBFEXYfn0a3/5daMDpdcIc7LGuwvU1LGWRb1pbW9HV1YXGxkYcPXoUDocDPh9LLBQiJkMKTIZGRkZw7tw5LFiwgK1DRBkgiiL+/devwP670KwdJkT5x+PxQK/XR93W3t7OLrICxW4yBerv78eePXvQ36/8mihEuUilUuErH7sZTR8MTQd+y38Jmx8/gjd97DLLFzqdDs3NzeHp90yEChtbhhTYMiSKIoLBINRqNVQqldzhEOUtURSx48ArcPw+1EI0rzrUQjSvmi1ERPmELUMKpFKpUFJSwkSIKMNUKhW2r70Z931gIQDgTV+ohegt/yWZIyOidGIypEA+nw9PPPEEB/oRZYFKpcKDH78FX3x/KCF6w3sJmxzPMSEiyiNMhhRIrVZj7ty5UKvVcodCVBBUKhUe+sQt+EJEQrTZcQR/ZkJElBeYDCmQVqvF2rVrodVq5Q6FqGCoVCr8f5+4BfeuDiVE571D2MSEiCgvMBlSoNHRUZw/fx6jo6Nyh0JUUFQqFf5p3S3YsnoBgFBCtPlxJkSU//x+PxobG1FdXQ2LxSJ3OGnHZEiB/H4/du/eHXc1ZCLKLJVKha+uWxJOiF7vC7UQcdo95TOj0YiOjg74/X60tbVNWqFe6ZgMKVBtbS0eeOAB1NbWyh0KUUGSEqLP374AQKiFaKOda5llW1NTExobG6FSqaDX6+FwOOQOKS+1tbUBCBWlbG9vhyAI4dvyBZMhBSouLoZWq0VxcbHcoRAVLJVKhX/+5JLwtPu3/Jewwf4cPD0XZY4s//n9fuj1elgsFrS3t8Nut8Pj8aCpqYkt5hmwd+9euFwumEwmmEwmbN++HR6PR+6w0orJkAL5/X48+eSTfNMTZVAy7y9p2v3ffCi0rMPbA8PY6DiC194JZDi6wtbS0gKPxxOuGG21WmG322Gz2SAIgrzB5aGNGzdGXVez2QwgufeIUjAZUqCioiKUlZWhqIhPH1GmrFmzJumE6B8/shj/YLoRANATGMEmxxG88vZAhiMsXA6HY1LSY7Va0dzcLE9AeU5KfiQ1NTUAkFetQ/w2VaDKykqsX78elZWVcodClJdaWlrgdruTfrxKpcI/mG7Clz+6GADQNziKzY4j6HyL6wemWz59ASud1+uVO4S0YTKkQMFgEN3d3QgGg3KHQpR3HA4HWltbU9r2bz60CA99/BYAgG9oDJ95/AiOv+FPY3SUT10zSiXNJMun54LJkAJ5vV7s2rUrr7JyIiD04drS0gKj0Qij0Qi9Xj9pUGxbWxuMRiNUKhVUKlXUfS0tLdDr9eH7Ynk8HjQ2NqKxsREWiwV6vR56vR4dHR3hfUdOGZZiiK2r4vf7wzOZIh/jdrtx3wd1+JdPLgEADAwHsfm/D+KhHd9EY2NjeLaTw+EIx2k0GqNaoVpbW6Puk2KLx+PxhM+juroajY2NCR8vXTe32w2PxwOj0Yjq6uqkZmD5/X44HI6o7S0WC6qrq8N1Z+J9MSZ7zGTOQ7pma9asCcckPX96vX5Si1Emrk2y+/R4PGhtbY16/pqamsLXq6mpacprPd17INXzTAe3252fs/bEAtbZ2SkCEDs7O+UOZUbGxsbEd955RxwbG5M7FKK0aW9vFwVBEO12e/g2u90uAhAFQRC7urrCt7tcLhGACED0+XyT9iPdF6mrq0sEILa3tyd1W7x9i6IoOp1OURAE0WazhW/z+XyiwWAQAYjNzc2iKIrij46cE6/53H+I5YtXh/fX3Nwsmkwm0Ww2izabTTSZTFHnF3mf2WyeMg673S6aTKbwdXG5XKJOpxMBRMVmt9vDtwMQnU6naDAYwrcZDIYpnpUQm80W3t5ms4mCIIhmszkcf+xzNJNjJnsekkTPb6avTbL7bG9vj7oudrtdNBgMotVqFZubm0VBEEQAotlsjntuyb4HUrl26RB5vSLjVDomQwpMhojyjc/nm/QlIJG+PCI/3KdKWCLvi9Tc3Bz38TabTXQ6nTPad7wvMukcIr8k9r5wXlzwlf1i6dV6EYB47fULJu1T2kYQBNHlckXdJ32pxn6xSQlB7L6cTmc49sgvTp/PF77dZDJFnXvsMROJt70ohr6ApXOIvC+ZY870PCK3SZQMZeLapBKndLvBYIjaLlH8M30PpBLTbEUmxYlinUp7e7tos9lEq9Wa9thmi8mQApOh/v5+8Wc/+5nY398vdyhEaSF96cfT3t4uWq3WqA/9yC+wZJMhq9Ua1XIT+fjIhGCqZEiKMzJ5iiQlXJHH/pn7DbFswbtFAGLdh7eI//vKO1HbSC1AVqs14f5i7zMYDJOSElGMvi6x5xnZ+pEKaft4yVPkl2Tkl9x0x0zlPKZLhjJxbVLZp5TAxNtnvGs50/dAKjHNRuT7orm5WTQYDCm1Pkmv91zDZc8VaGJiAsPDw5iYmJA7FKJZ8/v96OjogMFgiHu/VOhttiwWS3hwtNvtht1uh06ng06nS3of0liMRNts3LgxPPja7XbDYDBg/a3zsPhqLY6fA4ITIqw/OIb/3HQr1r5rLoAr05SnEjkexu/3h8cY6fX6SY+VYks0xmQm55ssq9UaHmvldrsnHSPeMWd7HvFk4tpkIs54x5jJeyAbMcWSxjnpdDrYbDYYjUb09fXNeD9+vz/hecqJyZACCYKAzZs3yx0GUVocO3YsK8cxmUyw2WxoaWlBR0cH9Ho9rFZr0oX6kpnSHfkhf+zYsfD/67UaAECxSoWxcRF/8xM3Ws0rYDbOm3J/8Zbcka6X2WyG0+mcNqZsEAQBgiDA7/cnPbEjE+ehlH0mOsZMH5+t10BbW1v4h8Bsj9fR0ZGT9aA4m0yBxsfHEQgEMD4+LncoRGmTjfoxzc3NaG9vD/9ydjgcWLhw4YxqCgHJxRqvxcdsnIeykiJMiMA/Oo/jh8+dm9FxZxpDNknnO9OWp0ych1L2OdtjZOs1cN999wEItQBKCX5NTc2MW56khKqxsTGt8aUDkyEF6uvrw2OPPZZSEyVRrols0p8qKUlXk7/JZEJXVxfsdnu4NUOarp1MnABw9OjRaR8frytAV38VfnDve3GVJtQo/08/P4lTF2ZWqVqKw+12T3lNsl0DRvpiXrlyZVKPz8R5KGWfiY6R7Hsgm68BaVq/IAiw2WxR98W2AsY7XlNTEywWCywWS7hVKR3d3unGZEiBBEHAli1buAYP5QWdThd+LUfW+Ink8XiwY8eOuPcl84EMYFJtF6vVCpfLFd4mmV/Z0rIEiVbslvYx1Vik2xbW4Cf3vRdCeQkA4KXzoXhFUZz2+NK+JYmuF3Dl13w2SOdtMBiS/lzKxHkoZZ/xjjGT90C2XgORNYUef/zxqOdWEIRJ9YxaWlrC7z+pDlRjYyOcTicef/xxOByOnEyEACZDilRaWor58+ejtLRU7lCI0kL6xdnR0TGpCGFbWxsaGxuxffv28G2RH8qxH8h2uz3879jEKDaJ0el04Q9n6QsmsnsrduDy448/Hr49XmE76djTjatYPk/AXuv7wmOJAODwydcxMRGdECVq/ZXGXDgcjklfhm63O1yoLxPijQmSzlu6PsnKxHkoZZ9A9LWc6XsglZhaWlpgsViS7l6TkimTyRR3fTKpQKRUlPPYsWPh1taFCxdGbSeday52kQHIwfltWaTUqfUDAwPir371K3FgYEDuUIjSJrJQXeRfvPo7onhlqjwu1/2xWq2iTqeLmnptNpvD21qt1riF6wRBmDQFObLonlTYTprWLBXGi40rXsE8iVSQMXaK/Nmei2LVotB9ZTe8W/yy8yUxOD4x6Rx1Ol3UdpFFHqU/nU6XcDp35HTr2U6t1+l0UectFQWMPe9kjjnT84g8HuKUPkhln5mKU3pc7PTzqY43k/fATGOKvG6xr6d4IsslJCo+GhundEzpvRavvlKu1ReSMBlSYDLk9XrF7373u6LX65U7FKK0khIP6QM7trZKLKmir1QRWfqg1el0YnNzc9QXSHNzs2g2m8OPNZvN4WQnllTJN3a/Ep/PJ1qtVtFgMIgGgyGcjMU+zuVyRSVt0pdjV1eX2NXVNem+q1Z8TLT824/Ed3r6omoWSYlU7BeiVO9Ful7xYpUqIEd+scZem2REfuGZTKbw9TGZTJP2NdNjJnMeLpcrqoKzlKxK1zOVfWYqztjK3FJC1N7eHnU8nU436fWXyntguphEMVQnKLJ69FT7jEzYpqolFHku0g8KqR5R7HZSgpSrVKKYZEd1Hjp58iSWLVuGzs5OLF26VO5wiKhA9QRG8NnvPo9X3g4AAD58cwN23m1AWUmxzJFdIa315nK5crJODCWvuroaPp8vLfuS6kpFjnlqbW1FV1dX1MDw6upqWK3WqG7sXMIxQwokiiLGxsaSHnBJRLmtXqvBHusqrLheAAD87yvd2LL7KAZHgvIGRnmno6MDGzZsSNv+YgfNR04ikEhjmqTxQrm42j2TIQXq6enBI488gp6eHrlDIaI0EcpL8eMvvherdKEB3M95+nD3d56Hf2hU5sgoX7jdbthstoy2zkgTEKTJCq2treEq2YIgwOFw5ORMaCZDClRVVYVNmzahqqpK7lCIKI2u0qjxvS234Y7F9QCAl97wY4P9ObzdPyxzZFckW2Gack9HRwfa29szegybzQaDwQCLxYLGxkaYzWaYTCYIggCn0wmr1ZrR46eKY4Y4ZoiIcsxocALbnMfxi+N/BgBcJ8zBj774Xiysq5AlntbW1nBXh9lsnlRzhkjp2DKkQIODgzh06BAGBwflDoWIMqBUXYT/2Phu3LNqPgDgLf8lWL79R3S+1Z/1WCwWC/bu3QuDwQCDwQCPx4M1a9aEi/ER5YOcWqjV7/fj2LFjOVuhMleMjIzg1KlTWL58OSoq5PmlSESZVVykwtc/tQw1FRr858HX0HtxFJsdR/Cdz63Ee3WTF3DNlFxZDJYok2RpGWpqaoJKpZr0V11dHXdxQyDUTNvY2IjGxkYYjcbweimFqKamBlu3bk14rYgoP6hUKjzQeBP++ZNLAACBkSD+6okX0HHqHZkjI8ovWW8Zksp2xyM1w8YyGo3w+/1wuVzhfmqLxYKFCxfi4MGDrHlBRHlty+qFEMpL8I/OExgJTqDpRy603rUcdxnnyR0aUV7IesvQjh07YLVa4fP5Jv1JiyZGslgscLvdcDqdUQP2pKbbZFabzjc9PT14+OGHObWeqICsv3UeHv8rIzTqIoxPiNjmPI7v/CG5NaaIaGpZT4akReUEQZj0F8vtdqOtrQ06nS5u68+GDRumbGnKV1qtFuvWrYNWq5U7FCLKog/ffDV++IX3QlsWatT/t1++jG/85jQLsBLNUlaTodbWVgChYkyRK/ImsmPHDgBIOKBaqmYprfZbKMrKyrBixQqUlZXJHQoRZdltC2uw1/o+1F0VWvH+v585g4ee7sT4BBMiolRlNRnasWMH/H4/WlpaYDQaUV1dPeVA6I6ODgChMUPxSK1FHo8nXAK8EAwNDeHIkSMYGhqSOxQiksGSayvx0/vfh+tr5gAAfvL8efzdnhcxGpyQOTIiZcraAGqPx4OVK1dGJS5SF5fD4UB7e3tUC5DH4wknSZFrnESKnE0lLRZXCIaGhnD48GEsWrQI5eXlcodDRDK4obYCbX99O/7quy/g9DsB/PLEBXgvjuJzt98QXlSVKJd9dOk1cocQlrVkSKfThcuA+/1+PPXUU7DZbOHEqLGxMWo15MiWnkRTyOMtDpdId3f3pAHHZ86cAQD0918pZNbT0wOtVouysjIMDQ1haGgIdXV1AIDe3l6Ul5ejvLwcw8PDCAQCqK8Plc33er3QaDSoqKjAyMgI+vv7UV9fD5VKBZ/PB7VaDa1Wi9HRUfj9ftTW1qK4uBh+vx9FRUWorKxEMBiE1+tFTU0N1Go1BgYGMDExAUEQMD4+jr6+PgiCgLq6OlitVgSDoUUcRVFET08PqqqqoNFoMDg4iJGRkfB1U8I5lZaWIhAIIBgMorq6mufEc+I5JXFOczCK72xagr/fdxovnvfi5bNv4m893Qgid1a7J0rk3L9/Qu4QwmSpMyQIAqxWK7q6uqLG+9x3333hf0d2nSVT9r2vr2/K+3fu3Illy5ZF/d15550AgF/96lfhxzkcDpw+fRoAcOLECXz/+98P3/f9738fJ06cAACcPn06auD2nj17cPToUQDAuXPnsGvXrnCysm/fPjz77LMAgLfffhu7du0Kd3EdOHAABw8eBBD6sNy1a1d47Z+DBw/iwIEDAEKtQbt27cLbb78NAHj22Wexb98+AEAwGMSuXbtw7tw5AMDRo0exZ88enhPPiedUAOf07KF2/OiL78XaxTVYX3YSNUXsPieaqZxYmyxy3RspnLa2NlgsFgCIajGKVV1dDb/fD6vVOuVKvIlahu68804cPnwYt99+O4Dc+tWX6JfswMAAdu/ejbvuugs6nU5Rv2Tz8dc5z4nnlCvndPTEKRxq/zXuWGeGVmBRVsptS6/NncXGcyIZAq4kNVLi09HREZ4tFjueKJLUN97c3DzjWWVKXah1aGgIJ06cwPLlyzlmiIjC+NlAlJqcWahVSnak5t+VK1eG70tm2Q29Xp+RuHJReXk5Vq1axQ87IorCzwbKB8888wzmzZuHL3/5y+ju7s7KMXMmGZKaoKUkKLIQo5QgxYocNF1Ii7sODw/j+PHjGB4eljsUIsoh/GygfPDiiy/irbfewje+8Q0sWLAgK0lRziRDUmITOVh6w4YNAICurq5ptymUafUAEAgEsH//fgQCAblDIaIcws8GyjeXLl3KSlKUM8nQsWPHJo35aWpqAnCl+GIsaar+9u3bMxtcjqmvr8dDDz0UHphJRATws4HyV6aToqwlQ9KMsXjjf1paWqDT6dDc3Bx1u8FggMlkgtvtjltHyOFwhKfpExERUX7LVFKUldlkfr8f1dXV4f/bbDY0NzfD4/HAbrfD7/fDZrPFrSfk9/vDK9MfPHgw/JimpiY89dRTOHjwYMJp99Nxu90wGo14+umnsWjRopT2IYf+/n786le/wsc//nFUVeXO1EQikhc/GyiXdHZ24nvf+x4mJhIvE1NRUYHi4ugioa+++mq4Xtd0iouLcdddd2Hv3r2zijVrU+vb2tqwY8eO8AKt0kr0TU1NSQ1+bm1tRXt7OwRBgN/vh06nS5hAJevnP/95uPAiERERKc/VV18dLqCaqpypMyQHv9+P3/3ud7j++uuh0WjkDidpUrFIpbVoEVFm8bOBlEav16OsrCzqtsceewzbtm1LuI1KpYIoitDpdPjqV7+Ku+++G2r17FYXy9raZLlIEAR86lOfkjuMlC1atEhRxSKJKDv42UD5SEqCFi5cmLYkSFLQyRARERHltkwmQRImQ0RERJSzFi5ciH/6p3/CPffck/YkSMJkiIiIiHKGNPtcp9NlPAmSMBlSoPr6evzzP/8zC6sRURR+NlA+uPvuu3HLLbdg5cqVGU+CJAU9m4yIiIgoZ5bjICIiIpIDkyEiIiIqaEyGiIiIqKAxGSIiIqKCxmSIiIiIChqTISIiIipoTIaIiIiooDEZIiIiooLGZIiIiIgKGpMhIiIiKmj/PxDuX779XFI9AAAAAElFTkSuQmCC",
      "text/plain": [
       "Figure(PyObject <Figure size 600x600 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject Text(0.47, 0.05, '\\\\shortstack[r]{Intensity for low\\\\\\\\customer preference, $\\\\lambda_d$}')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Figure 5(b): demand shock λ_d\n",
    "\n",
    "## range of λ_d\n",
    "xRange = range(0.1,1.5,length=800)\n",
    "\n",
    "## initialize output\n",
    "di = Dict()\n",
    "\n",
    "## compute\n",
    "guess=[0.99,0.99,] # set some initial guess\n",
    "for x in xRange\n",
    "    diPara = Dict(\n",
    "        :s  => 0.45,:mD => 0.1,\n",
    "        :λu => 0.1,:λd => x,:r=>0.05, # udpate λ_d\n",
    "        :values => (1.0, 0.9, 0.0), # yh, yd, yl\n",
    "        :searchB => (3.0, 1, 0.5), # ρB, nB, qB\n",
    "        :searchS => (3.0, 5, 0.0), # ρS, nS, qS\n",
    "    )\n",
    "    converging,θLO,θHN,diB,diS,diX = solveEquilibrium(diPara,guess=guess)\n",
    "    di[x] = Dict(\n",
    "        :converging=>converging,\n",
    "        :θLO=>round(θLO,digits=5),:θHN=>round(θHN,digits=5),\n",
    "        :diB=>diB,:diS=>diS,:diX=>diX\n",
    "    )\n",
    "    guess = converging ? [θLO,θHN,] : rand(2) # recursively update guess based on the previous eqm\n",
    "end\n",
    "\n",
    "## find thresholds for s\n",
    "s2p = xRange[findlast([di[x][:θLO] for x in xRange] .== 1.0)]\n",
    "s1p = xRange[findfirst([di[x][:θLO] for x in xRange] .== 0.0)]\n",
    "\n",
    "## set up canvas\n",
    "fig = PyPlot.figure(figsize=(5,5), facecolor=\"w\", dpi=120) # create figure\n",
    "fig.subplots_adjust(left=.1, right=.9, bottom=0.13, top=.93) # reduce white spaces\n",
    "ax = fig.add_subplot(111) # create axis for total trading cost\n",
    "\n",
    "## plot SMS volume ratio\n",
    "ax.plot(xRange, [di[x][:converging] ? di[x][:diS][:volume] ./ (di[x][:diB][:volume]+di[x][:diS][:volume]) * 1e2 : NaN for x in xRange], ls=\"-\")\n",
    "\n",
    "## reference lines\n",
    "ax.vlines([s2p,],ymin=49.0,ymax=100.0,linestyle=\":\",lw=0.8,colors=\"gray\")\n",
    "ax.vlines([s1p,],ymin=49.0,ymax=50.0,linestyle=\":\",lw=0.8,colors=\"gray\")\n",
    "ax.hlines([50.0,],xmin=minimum(xRange),xmax=s1p,linestyle=\":\",lw=0.8,colors=\"gray\")\n",
    "\n",
    "## set axes ticks and ranges\n",
    "ax.set_xlim([minimum(xRange), maximum(xRange)])\n",
    "ax.set_ylim([49.0,1e2+1])\n",
    "ax.set_yticks(50:10:100)\n",
    "ax.set_xticks([s2p,s1p])\n",
    "ax.set_xticklabels([])\n",
    "\n",
    "## format axes\n",
    "ax.spines[\"right\"].set_visible(false)\n",
    "ax.spines[\"top\"].set_visible(false)\n",
    "arrowed_spines(ax) # add arrows\n",
    "\n",
    "## label axis next to arrows\n",
    "ax.annotate(L\"\\shortstack[l]{SMS volume relative to total volume, \\%}\", xy=(0.03,1.0,), xycoords=\"axes fraction\") # left y label\n",
    "ax.annotate(L\"\\shortstack[r]{Intensity for low\\\\customer preference, $\\lambda_d$}\", xy=(0.47,0.05), xycoords=\"axes fraction\") # x-axis label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 6: Market's technology choices versus a social planner's under high search intensity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x600 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject Text(0.7, 0.03, '\\\\shortstack[l]{Asset supply, $s$}')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Figure 6(a): Low q^{SMS} (under high ρ)\n",
    "\n",
    "## range of asset supply\n",
    "mD = 0.1 # dealer size\n",
    "xRange = range(1e-3,1.0+mD-1e-3,length=1000)\n",
    "\n",
    "## initialize output\n",
    "di = Dict()\n",
    "\n",
    "## compute\n",
    "guess=[0.99,0.01,] # set some initial guess\n",
    "for x in xRange\n",
    "    diPara = Dict(\n",
    "        :s  => x,:mD => 0.1, # update s\n",
    "        :λu => 0.1,:λd => 0.1,:r=>0.05,\n",
    "        :values => (1.0, 0.9, 0.0), # yh, yd, yl\n",
    "        :searchB => (1.0, 1, 0.5), # ρB, nB, qB\n",
    "        :searchS => (1.0, 5, 0.0), # ρS, nS, qS\n",
    "    )\n",
    "    converging,θLO,θHN,diB,diS,diX = solveEquilibrium(diPara,guess=guess)\n",
    "    diX[:plannerθLO], diX[:plannerθHN], diX[:plannerWelfare] = plannerTechChoice(diPara)\n",
    "    di[x] = Dict(\n",
    "        :converging=>converging,\n",
    "        :θLO=>round(θLO,digits=5),:θHN=>round(θHN,digits=5),\n",
    "        :diB=>diB,:diS=>diS,:diX=>diX\n",
    "    )\n",
    "    guess = converging ? [θLO,θHN,] : rand(2) # recursively update guess based on the previous eqm\n",
    "end\n",
    "\n",
    "## find thresholds for s\n",
    "s1N  = findlast([di[x][:θHN] for x in xRange] .== 0.0);   s1 = xRange[s1N]\n",
    "s2N  = findfirst([di[x][:θHN] for x in xRange] .== 1.0);  s2 = xRange[s2N]\n",
    "s2pN = findlast([di[x][:θLO] for x in xRange] .== 1.0);  s2p = xRange[s2pN] # p for planner\n",
    "s1pN = findfirst([di[x][:θLO] for x in xRange] .== 0.0); s1p = xRange[s1pN] # p for planner\n",
    "\n",
    "## set up canvas\n",
    "fig = PyPlot.figure(figsize=(5,5), facecolor=\"w\", dpi=120) # create figure\n",
    "fig.subplots_adjust(left=.1, right=.92, bottom=0.12, top=.9) # reduce white spaces\n",
    "ax = fig.add_subplot(111) # create axis for total trading cost\n",
    "\n",
    "## plot thetas\n",
    "ax.plot(xRange, [di[x][:converging] ? di[x][:θHN] : NaN for x in xRange], ls=\"-\", color=\"blue\")\n",
    "ax.plot(xRange, [di[x][:converging] ? di[x][:θLO] : NaN for x in xRange], ls=\"--\", color=\"orange\")\n",
    "\n",
    "## vertical lines\n",
    "ax.vlines([s1,s2,s2p,s1p],ymin=0.0,ymax=1.0,linestyle=\":\",lw=0.8,colors=\"gray\")\n",
    "\n",
    "## fill\n",
    "ax.fill_between([0.0,s2],[1.0,1.0], facecolor=\"none\", hatch=\"//\", edgecolor=\"blue\", linewidth=0.0)\n",
    "ax.fill_between([s2p,1.0+mD],[1.0,1.0], facecolor=\"none\", hatch=\"\\\\\\\\\", edgecolor=\"orange\", linewidth=0.0)\n",
    "\n",
    "## set axes ticks and ranges\n",
    "xmin, xmax = 0.0, 1.0 + mD\n",
    "ax.set_xlim([xmin, xmax])\n",
    "ax.set_ylim([-0.005,1.05])\n",
    "ax.set_yticks([0,1])\n",
    "ymin, ymax = ax.get_ylim()\n",
    "ax.set_xticks([0.0,s1,s2,s2p,s1p,1.0+mD])\n",
    "ax.set_xticklabels([\"0\",L\"$s_{hn,0}$\",L\"$s_{hn,1}$\",L\"$s_{lo,1}$\",L\"$s_{lo,0}$\",L\"$1+m_d$\"])\n",
    "\n",
    "## reference lines\n",
    "ax.hlines([0.0,],xmin=minimum(xRange),xmax=1.0 + mD,linestyle=\":\",lw=0.8,colors=\"gray\")\n",
    "\n",
    "## format axes\n",
    "ax.spines[\"right\"].set_visible(false)\n",
    "ax.spines[\"top\"].set_visible(false)\n",
    "arrowed_spines(ax) # add arrows\n",
    "\n",
    "## label axis next to arrows\n",
    "ax.annotate(L\"\\shortstack[l]{$\\theta_{hn}$ (solid line) and $\\theta_{lo}$ (dashed line)}\", xy=(0.03,1.0,), xycoords=\"axes fraction\") # left y label\n",
    "ax.annotate(L\"\\shortstack[l]{Asset supply, $s$}\", xy=(0.7,0.03), xycoords=\"axes fraction\") # x-axis label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 600x600 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject Text(0.7, 0.03, '\\\\shortstack[l]{Asset supply, $s$}')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Figure 6(b): High q^{SMS} (under high ρ)\n",
    "\n",
    "## range of asset supply\n",
    "mD = 0.1 # dealer size\n",
    "xRange = range(1e-3,1.0+mD-1e-3,length=1000)\n",
    "\n",
    "## initialize output\n",
    "di = Dict()\n",
    "\n",
    "## compute \n",
    "guess=[0.99,0.01,] # set some initial guess\n",
    "for x in xRange\n",
    "    diPara = Dict(\n",
    "        :s  => x,:mD => 0.1,\n",
    "        :λu => 0.1,:λd => 0.1,:r=>0.05,\n",
    "        :values => (1.0, 0.9, 0.0), # yh, yd, yl\n",
    "        :searchB => (1.0, 1, 0.5), # ρB, nB, qB\n",
    "        :searchS => (1.0, 5, 0.06), # ρS, nS, qS\n",
    "    )\n",
    "    converging,θLO,θHN,diB,diS,diX = solveEquilibrium(diPara,guess=guess)\n",
    "    diX[:plannerθLO], diX[:plannerθHN], diX[:plannerWelfare] = plannerTechChoice(diPara)\n",
    "    di[x] = Dict(\n",
    "        :converging=>converging,\n",
    "        :θLO=>round(θLO,digits=5),:θHN=>round(θHN,digits=5),\n",
    "        :diB=>diB,:diS=>diS,:diX=>diX\n",
    "    )\n",
    "    guess = converging ? [θLO,θHN,] : rand(2) # recursively update guess based on the previous eqm\n",
    "end\n",
    "\n",
    "## find thresholds for s\n",
    "s1N  = findlast([di[x][:θHN] for x in xRange] .== 0.0);   s1 = xRange[s1N]\n",
    "s2N  = findfirst([di[x][:θHN] for x in xRange] .== 1.0);  s2 = xRange[s2N]\n",
    "s2pN = findlast([di[x][:θLO] for x in xRange] .== 1.0);  s2p = xRange[s2pN] # p for planner\n",
    "s1pN = findfirst([di[x][:θLO] for x in xRange] .== 0.0); s1p = xRange[s1pN] # p for planner\n",
    "\n",
    "## set up canvas\n",
    "fig = PyPlot.figure(figsize=(5,5), facecolor=\"w\", dpi=120) # create figure\n",
    "fig.subplots_adjust(left=.1, right=.92, bottom=0.12, top=.9) # reduce white spaces\n",
    "ax = fig.add_subplot(111) # create axis for total trading cost\n",
    "\n",
    "## plot thetas\n",
    "ax.plot(xRange, [di[x][:converging] ? di[x][:θHN] : NaN for x in xRange], ls=\"-\", color=\"blue\")\n",
    "ax.plot(xRange, [di[x][:converging] ? di[x][:θLO] : NaN for x in xRange], ls=\"--\", color=\"orange\")\n",
    "\n",
    "## vertical lines\n",
    "ax.vlines([s1,s2,s2p,s1p],ymin=0.0,ymax=1.0,linestyle=\":\",lw=0.8,colors=\"gray\")\n",
    "\n",
    "## fill\n",
    "ax.fill_between([0.0,s2],[1.0,1.0], facecolor=\"none\", hatch=\"//\", edgecolor=\"blue\", linewidth=0.0)\n",
    "ax.fill_between([s2p,1.0+mD],[1.0,1.0], facecolor=\"none\", hatch=\"\\\\\\\\\", edgecolor=\"orange\", linewidth=0.0)\n",
    "\n",
    "## set axes ticks and ranges\n",
    "xmin, xmax = 0.0, 1.0 + mD\n",
    "ax.set_xlim([xmin, xmax])\n",
    "ax.set_ylim([-0.005,1.05])\n",
    "ax.set_yticks([0,1])\n",
    "ymin, ymax = ax.get_ylim()\n",
    "ax.set_xticks([0.0,s1,s2,s2p,s1p,1.0+mD])\n",
    "ax.set_xticklabels([\"0\",L\"$s_{hn,0}$\",L\"$s_{hn,1}$\",L\"$s_{lo,1}$\",L\"$s_{lo,0}$\",\"\"])\n",
    "\n",
    "## reference lines\n",
    "ax.hlines([0.0,],xmin=minimum(xRange),xmax=1.0 + mD,linestyle=\":\",lw=0.8,colors=\"gray\")\n",
    "\n",
    "## format axes\n",
    "ax.spines[\"right\"].set_visible(false)\n",
    "ax.spines[\"top\"].set_visible(false)\n",
    "arrowed_spines(ax) # add arrows\n",
    "\n",
    "## label axis next to arrows\n",
    "ax.annotate(L\"\\shortstack[l]{$\\theta_{hn}$ (solid line) and $\\theta_{lo}$ (dashed line)}\", xy=(0.03,1.0,), xycoords=\"axes fraction\") # left y label\n",
    "ax.annotate(L\"\\shortstack[l]{Asset supply, $s$}\", xy=(0.7,0.03), xycoords=\"axes fraction\") # x-axis label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 7: Market's technology choices versus a social planner's under low search intensity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Hjh3DgQMHcN5552U7TsZobm6WQ7tMsNvt2LFjR8KnaITMInMm95G5KKQTKYAEQRAEQZhwsLsGaPLkydmOIAiCIAhCjsOuAPrv//7vbEcQBEEQBCHHYVcAnX/++dmOIAiCIAhCjsOuABIEQRAEQUg3UgAJgiAIgjDhkAJIEARBEIQJhxRAgiAIgiBMOKQAEgRBEARhwpF0AbR161YsWrQoFVkEQRAEQRAygunPAuvo6MDNN9+Mjo6OhD8EVBAEQRAEIZuYKoDWrFmD/v5+2O32FMcRBEEQBEFIP6YKoIaGBgDA/PnzsXLlypQGEgRBEARBSDdJXQNUWlqaqhyCIAiCIAgZQ+4CEwRBEARhwiEFkCAIgiAIEw7Td4GlnbfWAt450b9vOR84/auB/7e/Dhx5IrazaBrwsdsC/z82CLz5s/hZzrkVmDwrKNuvAFd/7Nec+iWgNKg9wAe/B6ztsV8j26SRY9vk8wF/bp+G598PbNOkvEFcVhp/m/5iuxWjvsA2XWL5FUoKYm/TW0NfQrcrsE1nT/09TimOvU09rvPx5lBgm+ZMeh3nTou9n1y+adhjk20CjG2TbWwe/uq4EUBeXJ8gqMKdd2Y7gXmyWgD19vair68v5LF33nnnxBfrAGeMF5+xfPwidOCu2D9wysnjF9Z4rwGAM28IXVj//itg8O3Yryk5JXRhPfp7oOvB2K+RbdLIsW3KB3Cm82TcdVdgm062DOIHa+NvU/09N+DtY4Fteuu+X+HsObG3aeNTp+DBXYFtavqn3+PKz8Xepo27l+OutYFtWn7Z67jln2Pn+4ftZHxetgmA8W16qc2G/332u3F9gqAKKhdAWT0F9sADD+DjH/94yL9rr702bT+PXDYsXzqGvDygthYY85iQ+MaA0e6UZwMA6n0lJJ/Ha0YyZvrnD3um4sX+KzHsmRrx+76/N4fk8/lM/BCfmUE3qN77LyH5iExIyMxGxWfUV4wrr3wRU6cOp8Uv8EafWzcvac52lJxj6tRhmVuCOSgJWltbCQBZLBZTr+/p6aEDBw6E/Hv66acJAO3/TRVRC2jLrTW0rNZNbncSQb1u8r5UI7449Pf309q1a6m/v59lPhV9b7xBBBCVlvbT3bffTv3P1RF5kwmn5aM/avnojzXiU8DXv/tOWnvPLdT/cCmRy8Yun8q+qO9bghCHrJ4CmzNnDubMiXydz+pNjbjp7BmovbQV1acC+QUtAIpM/ZwxbxFuXNuC6+ZCfDEoLS3FLbfcwjafir59+7T/Wq2l+Py8MZQebwF2u4HLWoB8c/mQX6S9HgCOtGr/FR9rX+npi3DLGV/S/sf2V2DuFazyqeyL9r4lCPFgexH09ueKMGNGi7b4fNAK7IapSTY2BtTVAa2tRUCt+MSXWV970HW65y2/G+h4l8WiIb4M+2YuAAqmAJYLADJwGlj17c22TxCMkMzho2RPgUXiwIEDBICqqg5opx2SODzqdhPV1GinIGpqSHxxfL29vXT33XdTb28vy3wq+ubO1Xzz5p0Y22NHWZ0+EF9mfL09PSFzi1s+lX3h71uCYBS2BVBn54HAgyYmRcTFTHwxfaOjo7Rv3z4aHR1lmU81X3W15gOIvva1wNhyWDTEl1lf8NzimE9lX1JjK0xo2BZABw4cCP1GApMi5mImPvFl0Kf/+/nPzfsMIT7xiU8QEiKpAqihoYEAUJJn0kKIWgARGZoUhhYz8UX0DQ8P0yuv7KFly4ZZ5lPNd/nlgQLoueeGac+ePTQ8PJyQLyHEx9Y3/MIy2rP7jzR8bD/Re1uIht9nlU9l3/BwhLklCAYwVbm0traGFD8AqLKykhobG6mrqyupQDELIKKYkyKhxUx843zd3X10++330axZfSzzqea7665AAfTOO3103333UV9fn2GfKcTH0tf38Cy6754fUN/DszR318Os8qns6+uLMrcEIQ6pO3STIuIWQEQRJ4WpxUx84kujT78G6PTTE/clhfj4+jbma//d+/945psIPkE4gZoFEFHIpPC+pDWrM7WYiU98afLNn68VQF/5SmI+louG+FLnawHR85/mm28i+ASBstwIMSlO9I3w+bQ+LdfNBVDbgg2PFaHITOsI8WHSil/iggUtGBj4OmbPns0un0o+hwPo6tKeumABcPz4cTzyyCP4+tejjC33viriS8p33DqAR/7yCXz9tLcx27MPOL4H8LqAgsks8qnsizu3BCEK6hZA4N0BWEXfF+YW4EDJd1BSUsIyn0q+/fsDz1uwACgpKcGnPvWp2GOr8CIkvtgE9n8RcOBftGaIf7wO+MxvWeRT2WdobglCJLJ9CCoco6fAgq+5WFYb+Owms4dHxSe+VPp+9avABdDvvZegnPvpA/GZd/XuDpwG45hvIvqECYuSBZAKHYBV9I2+sJT2rbuQRl9YyjKfSr5vfsNLAJHFQuTzmWjWxn3REF9CL/fvf0cfUUue5vn9x9jkU9k3+sJSaYQomEK5AkiFDsCq+nqPHaW7f3wH9T5UxjKfSr6Ks7sIILrySu1bptr1K7QIiS82Ifv/9+dojl1fYJNPZV/vQ2XyURiCKZQqgFTqACy+ietz71pGkwqdBBD963c9UX2GUGQREl8C/HEp0VOnEu35Js98KvoEwQTKFEAqdQAW38T2/bXT7b/+55Hbf8V30RBfdnweF+98KvoEwQR8C6D9nf7HVOsArKqvv7+f1q5dS/39/SzzqeJ75JHABdB/vfd8oj/WUH/fscDYmkGFRUh8UQmZWwzzqeyLO7aCEAW+BdCGKu10gpnFTCfCJBNf9JcPDQ3Rrl27aGhoiGU+VXz/+q/a8ydN8pF71zKiFtDQzmW064UdgbE1A+NFSHyxfePmFrN8KvsMja0gRIBvAdSobgdg8U1s32c/qxVAFRXEdtEQX5Z97kGi3leI/vE8z3yq+gQhAdg2QvTNrVKyA7DKPpfLhffeew9nnnkmJk+ezC6fCj4ioLNT+3rBgoBv1J2PI2904kzP1zD5M4+yby4nvtT6xs2tV2qB7u3AjPOAaw5kPZ/KvojvW4JgALYF0OpNjbjp7BnKdQBW2TcwMIDNmzdj1apVmDNnDrt8KviOHAHsdu3rBQsCvm//5ucon/8gVhWtxZzCuqwvGuLLrG/c3Jq5QCuAHAcBzyhQOCWr+VT2RXvfEoS4ZPsQVDj6KTDggJIdgFX2+Xw+crvd5PP5WOZTwff004ELoP/4x4AvL89Hbucw+V5mfPpAfGnzjZtb723RntcCouOvZj2fyr5I71uCYAS2BVBV1QElOwCLb2L7fvSjQAF0/Hiojyi5fBERn5q+gbcCBdDbzfzy5YpPEGLAtgDq7AzqA2RiUuRKR+FM+qxWK61fv56sVivLfCr4vvIVzTF/fqivtzcwtuwXDfGl3Bc8t4iIyOcl2jJVe86rt2Q9n8q+cWMrCAZhWwCN6wSdwKTIlY7CmfY5HA7atm0bWa0OlvlU8J1xhub58IdDffrYOhyOhPMZQnysfQ67NXT/ExE9d5n2/ec+mfV8KvvGzS1BMIg6BRCRoUmRSx2FxaeWr78/cPorVfkSQnxq+V69RfvelqlEXgMfmaL69mbbJwhhqFUAEcWcFNnuAKy6b2jIRV//+mEqKnKxzMfd9/zzgeLn8stDfS6Xiw4fPkwul3wMwkT0uR4tosO//zq5RoOa9b39YOA6oIG3sppPZV/UuSUIcVCvACKKOCk4dABW3bdiRQ/deeedtGJFD8t83H0XXhgogN59N/QlPT3a2Pb09BjyJYX42Pl6tq/Q9v/2FQHf8b1Em4qIti0g6vtLVvOp7Is5twQhBmoWQEQhk4JLB2DVffn5Hlq+3EGjoyY+wVzB7U2HDyAqKyMKvyPX4/GQw+EgjyfK2Cq+CIkvNh73KDl2LCfPY/kBn9cz/oNRs5RPZV/cuSUIUWDbCDEuzDoA54SvugWPPDKNbz7mvu0ztmBgIA8LFgB5eaFPLygowLRp0+L6ALBoLie+1PoKioox7bOPALs9ob6CSSzyqeyLO7cEIQrqFkDg1QE4F3yVs4uwtfULuPoL18BisbDLx9n35Yrfoe4BL4BCfwfoYOx2O7Zv346rr746+tgqvAiJLzb+/X/VA7AwzKeyz+4Yjj+3BCECyhZAY2NAXR3Q2loE1LZoi9kHrcBumJpk4gMK330Gxc45yMfnE3JlKh9nX7nnh/B4tem08EIPwqdWfn4+iouLkZ+fH1us6CIkvjgaff8XTgr1EQEX3gMM/A2YeyUwyZKVfCr78j++ztjcEoRwsn0OLhwj1wBx7AAsvont+3XzmP8aoDce/a5S11CIL8s+/d/R7TzzqeATBBMoVwBx7QCcC74x1wj1bF9BY48WsszH2fed72iPTZnsJM+j+eN8Y2Nj1NPTQ2NjY3F/RjL5xMfTF3H/e91EL345UAC9/pOs5VPZN/ZoYeJzSxBIsQKIcwfgXPD5byfdvoJlPs6+yy/XHr/kEm9En+lbdRVZhMQXm6j73+sm2jxF8z11as5sbyZ9/hYDchu8kCDKFECcOwDnis//V6prhGU+rj6vl+ikk7TvrVwZ2WfqCFAC+cTH2xdz/++6JnAUKEe2N5O+MdeIHAESTMG3ANrf6X+Mewdg8U1s39tvBxogrlsX32cK8eWu76//GXotELd8KvgEwQR8C6ANVUReNToA54pvYGCAnnzySRoYGGCZj6uvtTVQAO3ZE9k30Lacnnxia2BszcB9ERJf1KeOm1vBHHkyUPzs+GxObG8mfTHHVhBiwPc2+O7n4XulDjeubUFraxFqaoCWFiTWtC7slkmfD+KL4fP5fHA6nfD5fCzzcfXt23fi5fnABRdE9vm6tsE5dAp8nisSCBQ7HwD2tyiLT2Pc3Apm5oLA16deB0yerfz2ZtIXc2wFIQZsCyDf3CplOgDnis9isWD58uVs83H1dXZq/z37bKCkJLJvuq8Oyz/4GfDGERaLhvgy64s4t3SmngkUzQDGBgD76zmxvZn0xRxbQYgB2wJo9aZG3HT2DPYdgHPJ5/V6MTIygpKSEhQUFLDLx9WnHwGK1AE64NuA5vpClBzeggIg64uG+DLriza3AGifmzJzATDwBlBQnBPbm0lfzLEVhFhk+xxcOPo1QMABWlbrJu9LyZ0jDr6GQ3yxfZFu1eWUj6Ovpydw/c9//3d0X1lZYi0GDMH0mgzxjffFbYPgHhz/CboKb28mffJp8IJZ2BZAVVUHlOgAnEs+l8tFhw8fJpfLxTIfR99zzwUKoOeei+6rrT0xtqNDLBYN8WXWFz63uOVT2Wd6bIUJD9sCqLMzqA+QiUmhakdh8anla2wMFEDHjqUvX0zEJz7xCULCsC2AxnWCTmBSqNxROJs+h8NB27ZtI6vVwTIfR9/y5drrTj45tk8fW4fDkXA+Q4iPtc9ht4bu/0iMjRAdf5Wob8/47ym2vZn0jZtbgmAQdQogIkOTQuWOwtn2Wa1W+vWv19MNN1hZ5uPo+9jHtNd+4QuxfVarldavX09WqzWhfAkhPrY+6/M30Pr1vw7d/+FsqzjRC2hxxvOp7Is4twTBAGoVQEQxJ4XqHYXFp5ZvuK2O8vN9BBD94Aepz2cK8anr23OT9rzW0vEXRHPIlws+QQhCvQKIKOKkyIWOwtn3+aiw0E01NT6m+Xj5/nzXJf7rfzZujO3z+XzkdrvJF2lh475oiC9pn+/lGnI/Wki+l2P43vyF9jNbQDR0JKP5VPbFnFuCEAM1CyCikEnhfamGltW6zS1m4vP79Fu1jx41cTupgtubrO///q3JXwBdc3VsX9xbdRVfhMQXm57uo9r+f6gsuq/npUAB9P7vMppPZZ/cBi+YhW0jxLgw6ACca778xQ+hpmYZZs2awTIfN1+n458AACcVD2LF/G9hWu1jUX0zZszAsmXLMGNGlLFl1lxOfKn1zZg5C8uW1mDGsTeBI1si+ywXBr62dQKnfilj+VT2xZ1bghAFdQsgZL8DcE76Pmr+TUrJ7U3C17lP6zr74ZlHseyTj6P2VIrqmzx5Ms4555zYQoUXIfHFZvLkyTjno+cCZz8K7PZF9k2aAUydBwy/C9j2ZTSfyj5Dc0sQIpHtQ1DhGD0Flu0OwLnoG3pkKu165BYacthY5uPkGx0lKijQfB85yxPXNzQ0RLt27aKhoaH4coVPR4gvMiH7P5bvpeu0x5+el9F8KvuGHDbjc0sQglCyAOLQATgXff3P1dHae26h/ufqWObj5LvqqkADxKam+L7+/n5au3Yt9ff3G/shii1C4ovNuP0fzbf/x4HrgFy2jOVT2df/XF1ic0sQTqBcAcSlA7D4xKf/e+01876YiG/i+d7/HdHGfKJnziOyv8kvH1efIJiA3W9OrAKIUwdg8U1s3znnaP8tKNBOh5nxGUJ8E8vnHtQ6QnPNx9UnCCZQpgDi1gE4F329vb109913U++xoyzzcfItWaJ9ff75xnz+se3tjRHEfD7x8fbF3P8M8qns6z121PzcEiY0fAug/Z3+x7h1AM5V3+joKO3bt49GR0dZ5uPic7mIysq0///a14z5RocdgbE1A/NFSHyxfSFzi2E+lX1xx1YQosC3ANpQReTl2QFYfBPbd/Ro4Pqfn/0sMV9SiG/i+F78onY9kMfFMx9HnyAkCN8CqJFvB+Bc9Q0PD9OePXtoeHiYZT4uvj/8IVAA7dxpzDe8YQrtaVlJw4N2E6Ei52O5CIkv4tMizq1ovmcvDdwJdvzVjORT2Wd4bAUhDLaNEH1zq9h2AM5V38jICP70pz/hrLPOQklJCbt8XHydnYGnL1hgzDc09A386bWTcNaf/hklSx5m31xOfKn1RZxb0XyXrAO2L9T+f+8tQNVu5bY3kz7DYysI4WS7AgtHPwJ09VWdtOXWwF/iyfyl4XZrze/EJ75U+KqrtaM/p5+emI/bX87iY+rzjhFtLg4cBeKWTwWfIBiAbQEEHGDZAVh84jvrLO37X/5yYj72i4b4+Pi2X6x5ts7mmU8FnyDEgW0BVFV1gGUH4Fz29fX10X333Ud9fX0s83HwDdjc/ut/fvQj4766uhNj29PNe9EQX1p84XMrLn++WXM8Pp3o5WrltjeTvoTHVhBOwLYA6uwM6gNkYlKo1FGYiy/4YkKO+Tj4/rj2h/4C6KmnjPvs9qALNRksGuLLrC/hC3XfWhs4BTZwULntzaRPLoIWzMK2ABrXCTqBSaFSR2HxqeX71df/2V8AvftuZvIZQny55evdHSiAjjzBL59qPkGIgDoFEJGhSaFaR2FOvtHRUWpv30dLl46yzMfB90/X7CCAyHLSIPk8xn0Rm7VxXzTElzLf6AtLaV9Hu/Fmfe5BopY8Lcu+H6Y9n8o+aYQomEWtAogo5qRQraMwN9/Ro710xx13U1lZL8t8HHwVFT4CiK782AsJ+aJ+FEKOLELii+3rfaiM7v7xHdrHzBjld2cTPW4h6vx+2vOp7EvqY2aECY16BRBRxEmhYkdh8anlc7uJJk3SXvPdmmfSmi8pxJcbvtE+Ip+Pbz5VfYJwAjULIKKQSaFqR2HxqeXbvz/QAfo3D42lLR/LRUN84sslnyAQ407QccmBjsLcfEV1v8InPvkEBgeXobS0lF2+bPuCO0AvXFQIfNy4z2q1YvPmzVi2LMrYMuywK77U+az2QWz+6xVY9pFJKD3Swi6fyr64c0sQoqBuAQRgzFuEG9e24Lq5QO2lrag+FcgvaAFgbpJNdN/VcwrQXvAtTJ48mWW+bPv27dP+O2kS8NGPJuabPHkyzj333Nhjq/AiJL7YTJ48Geeedx4mL6oD/uo27vO6AcdBIK8AsJyXtnwq+wzNLUGIRLYPQYVj9BRYLnQUFp9avs9+Vnv9woWpzxcC99MH4suc76nTtef9qY5nvlzwCRMWJQugXOgozNHn3LWUDjadTc5dS1nmy6bP5yOyWDTHTTcl7nM6nXTw4EFyOp3GQnNfNMSX0MvH7X+jvhc+rz3nmXPTmk9ln3PX0sTmliCcQLkCKFc6CnP09XQfpTvvvJN6HipjmS+bvvfeC1wAvWBB4r6enh5tbHt64mZOZnvFx9MXcf8b8XX+h/b9jflEYyNpy6eyr+ehssTnliCQYgVQLnUU5ujz+Xzkdg6T72We+bLpe/rpQAFkxufz+cjtdpMv0m3NsVBkERJfbKLu/3i+97Zo32sB0fFX05ZPZZ/v5Rpzc0uY8ChTAGW7A7D4JrbvP/8zUPxce23yvoQQ38T1DbwVKIDebuKXj4tPEEzAtwDa3+l/jEMH4Ings1qttH79erJarSzzZdN3yimab+pUcz7r8d7A2JpBhUVIfFEJmVuJ+Hxeoi1TtcdfXZW2fCr74o6tIESBbwG0oYrIy6MD8ETxORwO2rZtGzkcDpb5su0DiK67zpzPsWM5bdv2TGBszcB4ERJfbN+4uZWI77nLtMee+2Ta8qnsMzS2ghABvgVQI58OwOITn/7vJz8x7+O0aIhPId+rt2j/v2UqkdfDLx8nnyAkANtGiL65VWw6AE8Un9vtxrFjx/ChD30IkyZNYpcvW76eKzdi14vaVFm40JzPOZaH3rf+jA95V2DSpx9j31xOfKn1RZxbRn1zP6f9t/hkwNULTDk55flU9hkeW0EIg20BtHpTI246ewaLDsATxWe32/Hwww9j1apVmDNnDrt82fJ94LsGu178OgBgwQJzvpUP/w/K5z+IVflrMaegLuuLhvgy64s2twz5fB7gq/3AZJMf85AD4xeLhMZWEILJ9iGocPRTYMABNh2AJ4rP4/GQw+EgjydwmJ1Tvmz5bvz0bwggmj3bF/HDuY348vM95LD3k0c6Rk9IX6S5xSmfyj5TYysIxPgaoKqqAyw6AItPfBfMf5cAoiUX70vKR5RcvoiIT3ziEwRTsC2AOjuD+gCZmBRcOwpz9tlsNtq4cSPZbDaW+bLhczqJCgt9BBD9+zWNpn19fYGxZb9oiC/lvuC5lZSv7QoltjeTvqTGVpjQsC2AxnWCTmBScO4ozNk3MDBATz75JB0/PsAyXzZ87e2BO8Ba7viFaZ8+tgMDAwnnM4T4WPsGbMdD93+iHPyl5moB0YtfYr+9mfSNm1uCYBB1CiAiQ5OCe0dh8anlW78+UAD97fXM50sI8eWur3tHoADimE8FnyCEoVYBRBRzUqjQUZizb2RkjFas6KHCwjGW+bLh+853tO9PmULk8Zj3jY2NUU9PD42NjRnOZwrxsfSNPVpIPdtX0JgrzgeaRsN5PFD8PPNx9tubSV/UuSUIcVCvACKKOClU6ijM1bdihfaJ1StW9LDMlw3fpz6lPeeSS5Lzxfw0+BxYhMQX29ezfYW2/7evMO976jQt08vV7Lc3k76Yc0sQYqBmAUQUMilU6ijM2VdYqB0BGhkx8ZeUgtsbz+f1Ep10klbUrFyZnC/uX6mKL0Lii82Ya0Q7AvRooXnfi1/S8vzubPbbm0mfHAESzMK2EWJcFOwozN53fQseemgO33wZ9r3zDjA0pH09rgN0gr7CwsLYTdqYNZcTX2p9hZOmYE7VQ8Bul3nfzIXA0d8Dg28DXhfr7c2kL+7cEoQoqFsAQb2Owtx9VbML8czvv4rFlUswffp0dvky7evsDHwd6SMwEvE5HA7s3LkTixcvjj62Ci9C4ouNf/9/dh2mm/XNXHDiCwLsfwXKPsV2ezPpcwyNxp9bghABZQugsTGgrg5obS0Calu0xeeDVmA3TE0y8QF4dxucQ6fA57kiIVem8mXapxdABQXA+ecn5/P5fHA6nfD5fLGDKroIiS82/v2PAvO+mUFVuLVTK4CYbm8mfb5zHzA2twQhnGyfgwvHyDVAqnYUFp9avqoq7TXnnZfefBERn/jC8fmIWmcSbS4hev1ufvmy6RMEEyhXAKnaUVgFn8c9So4dy8nzWD7LfJn0+XxEZWXa61asSN4nnwU1sX0R978Z39ARIm+U3yFG25tJn+exfPksMMEUShVAKncUVsHnv510+wqW+TLpO3o00ADxZz9L3mf6Vl1FFiHxxSbq/meST2Wfv8WA3AYvJIgyBZDqHYVV8LlcLjp8+DC5RodY5suk75lnAgXQCy8k7/OPrcsV44Xmt1d8vH0x9z+DfCr7XKND5ueWMKHhWwDt7/Q/lgsdhcWnlu8nP/b4C6Bjx1KbzxTiE5//uR4i+xtEg10882XDJwgm4FsAbagi8uZOR2EVfA6Hg7Zt20YOh4Nlvkz6rr/izwQQnXFGanyOHctp27ZnAmNrBu6LkPiiPnXc3DLr83mJts7WnrP31pTlMwRTn6GxFYQI8L0Nvvt5+F6pw41rW9DaWoSaGqClBYk1wQu7ZdLng/hi+DweD7q7u+HxeFjmy6Sv840yAIDH40Nra37SPk/XdnTbToXnkksATEtAEtmnyi3K4tMYN7fM+vLygZPKAddxwNY53mEynyGY+gyNrSBEgG0B5JtblTMdhVXxzZw5E9/85jfZ5suUz35uC97t01582amtKPjU9Unnm+GrwzeLfgq8+R6LRUN8mfVFnFtmfTMXAv2vArZ9APm0oijJfIZh6DM8toIQBtsCaPWmRtx09oyc6Cisio+I4PF4UFhYiLy8PHb5MuV7rSPwuq9d/hiu+fITKcj3GDbckofCw63IA7K+aIgvs75oc8uUT2+I6BkCBruA6R9JOl9CMPMlNLaCEEyWT8GNQ78GCDhAy2rd5H0puXPEwdeEiC+2L9KtupzyZcp34YWBO8AOt65MSb6yssRaDBiC6TUZ4hvvM9UGIZqv7y/aYy0gem9LSvKZgolPPg1eMAvbAqiq6kDOdBRWxed0OungwYPkdDpZ5sukDyCaPZvI50mNb+nSE2M7Mshi0RBfZn3hcysp39gI0cYC7bHO/0hJPtMw8JkeW2HCw7YA6uwM6gNkYlJw6SgsPvV806dr/12yhE++mIhv4vmeOU/7/xeu4pmPk08QosC2ABrXCTqBScGpo7BKvqGhIdq1axfZbEMs82XCd/31RAUF2tcNDanLp4/t0NBQwj5DiI+1b8hhC93/yeZ75Qbt6yfmaJ/bkqyP+fjF8o2bW4JgEHUKICJDk4JbR2GVfP39/XT//Wuprq6fZb5M+HbvDpwC27Qpdfn6+/tp7dq11N/fn5AvIcTH1tf/XB2tvf/+0P2fTL4/XKj996nTiVy25H3Mxy+WL+LcEgQDqFUAEcWcFBw7CotPLV9TU6AAOngwu/lMIb6J43vxy7zzcfQJQhDqFUBEEScF147C4lPL9+1va49NnUrk9WYvX1KIT3ziE4S4qFkAEYVMCu9LNbSs1m1u8RGf31dW1kt33HE3HT3ayzJfJnyf+IRWxFx2WWrz9fb20t133029vVHGlvuiIb6kdL3HjtLdP76Deh8qY5lPZV/cuSUIUWDbCDEuCnQUVs2Xv3g9vvKVL6K01MRHNSi4veE+rxfYv1/7euHC1OabNm0avvjFL2LatChjy6y5nPhS65s2oxRf/OJXMM3+FnBkS+ry+cZYbm8mfXHnliBEQd0CCPw7CivpW2D+TUrJ7Q3yvfUWMDqqPTduAZRgvuLiYlx44YWxhQovQuKLTXFxMS5cWAH4HgV2+1KT7+8PAB88nZJ8AFiPXyyfobklCJHI9iGocIyeAlOho7BqvuENU2hPy0oaHrSzzJdu32OPBS6Abm9Pbb7h4WHas2cPDQ8Pxw+r8OkI8UUmZP+nKt/7v9McLSBqu4LV9mbSNzxoNz63BCEIJQsglToKq+Tre7aO7vvJbdT3bB3LfOn23Xab9pzCQqJYTWXN5Ovr66P77ruP+vr6jIVWbBESX2zG7f9U5Bt+P1AAvfFTVtubSV/fs3WJzS1BOIFyBZBKHYXFp5bvc5/zEqB9Flim8sVEfOKLhc9HtHW29vo/fY1fvkz6BMEE7H5zYhVAKnUUFp9aPt9joJnTBgkguummzOQzhPjEF4udS7TXPvNxnvky5RMEEyhTAKnWUVhFn/8wfU83y3zp9L3Xusp//c///sKT8nwJnwJLcHvFx9sXc/8nk6+jQXvdxgLtQ1KT9UWCua+vp1tOgQmm4FsA7e/0P6ZiR2EVfUYv1MyV7Q3mqSfG/AXQy/f/Z8rzJX2hJvNFSHyxfXEvgjeb791NgeuAjr+avC8ajH0J3WAgCEHwLYA2VBF51e0oLD61fHfcEbgDbODBaWnLlxTiE184AwcDBdDbTfzyZdInCAnCtwBqVLujsIq+0dFR2rdvH42OjrLMl07fl76kFTlnneVLS77RDcW07/Fv0eiww4RkvI/tIiS+iE+LOLdSkc/nJdoylWjTJKID/5W8Lx4MfYbHVhDCYNsI0Te3StmOwqr6BgcH8cwzz+CUU05BcXExu3zp9HV2av9duDAvLfkGBr+JZ/bOxSnTbkFx1UPsm8uJL7W+iHMrFfny8oGr/wpMPT3yc3Jk/GJheGwFIZxsV2Dh6EeArr6qk7bcGvhLPJm/NNxurVmd+MQXib6+wOmve+5JXz5ufzmLT3w56xMEA7AtgIADynYUFp9avra2QAH0u9+lLx/7RUN84sslnyDEgW0BVFV1QNmOwqr6+vv7ae3atdTf388yX7p8a9YECiD9WqBU56urOzG2fcd4LxriS4svfG6lPJ/PRzTYRTR0JDW+VOdLo8/02AoTHrYFUGdnUB8gE5MilzoUZ8o3NDREu3btoqGhIZb50uVbvlzzFBenL5/NFhhbDouG+DLrC55bKc/nHQt0hO74t+R9qc6XZl9SYytMaNgWQOM6QScwKXKpQ7H40u8755zAESCO+Qwhvont+8MF2vd2LOaZL9s+QYiAOgUQkaFJke2Owir7nE4n/e1vB2npUifLfOnw2WyB4uejH01fPqfTSQcPHiRn8Kescl80xJcyn3PXUjr45t9C938q8+3+uvZ4a6l2OixZX6rzpdEXcW4JggHUKoCIYk4KDh2FVfYdPdpDd955J5WV9bDMlw7f5z4XKIA2bUpfvp4ebWx7enoM+0whPpa+nofKtP3ffTQ9+d78hfZYC4iGDifvS3W+NPqizi1BiIN6BRBRxEnBpaOw2j4fFRa6qabGxzRfenz6v3feSV8+n89HbrebfJH+Os+BRUh8sX2+l2vI/Wgh+V5OU76elwIF0PtPJ+9Ldb40+mLOLUGIgZoFEFHIpODUUVh8avnmzdOKlunTibxeHvlUXITEl2Wf83igAPrrj/jl4+YTBGLcCTouTDsKq+wrqluLKz77ewwNXYeZM2eyy5cO39/fKcK77wILFgD5+enLZ7PZ8NRTT+G666KMLcMOu+JLnc82MISn/n4VrjutCDOPbExPvpPmA0NdgK0zNT5G4xfLF3duCUIU1C2AAIx5i3Dj2hZcNxeovbQV1acC+QUtAMxNsonuu6qsELs9dSgsNPdrodr2fuVDBZh+9UYAeVi4ML35CgsLcfLJJ8ceW4UXIfHFRtv/p6DwkvuB18fSk2/KqdrXZgqgSL5U50uTz9DcEoRIZPsQVDhGT4Fx7CgsPrV8f733fP/1P7/5TXbzhcD99IH4+PpaQPTEh4jcgzzzcfQJExYlCyCuHYVV97lerKXDvz6dXC/WssyXat9vfnC/vwD6a2d687lcLjp8+DC5XC5jP4T7oiG+hF4+bv+nI9+LX2azvZn0uV6sTWxuCcIJlCuAOHcUVt3X031Uu530oTKW+VLt+5f/5yGAaHLRKLl3LUtrPlO36iq0CIkvNhH3P6N8Kvv8LQbkNnghQZQqgLh3FFbd5/F4yGHvJ4/B0zmqb+9nPqN9b9E576Q9n8fjIYfDQR6PJ6o/IoosQuKLTdT9zySfyj7PSzXm5pYw4VGmAOLeUVh8avm8Xu3Wd4DoW//kzXi+hBCf+BLBZSPaWck3Xzp8gmACvgXQ/k7/Yyp0FM4Fn81mo40bN5LNZmOZL5W+d94JNEBcuzb9+Wz9fYGxNYMKi5D4ohIyt9KZ782fE23MJ2rJI3rpupwZv1jEHVtBiALfAmhDFZFXrY7CqvsGBgboySefpIGBAZb5UulrbQ0UQLt3pz/fQNtyevKJrYGxNQPjRUh8sX3j5la68h3eGrgbrOflnBm/WBgaW0GIAN8CqFGtjsLiU8v3gx9oRU1eHtHQUGbycVo0xJejvsGuQAH01lp++dLtE4QEYNs5yje3SqmOwrng83g8sFqtKC0tDTQVY5Qvlb7OE73izjkHmDo1/fncY3mwv70Lpd4VKPz0Y+yby4kvtb6Icysd+abOA4pmAGMDWkPEHBm/WBgeW0EIg+1vy+pNjbjp7BlKdBTOFZ/VasW6deuwatUqzJkzh12+VPr0AihSB+h05Lv54f9B+fwHsQprMaegLuuLhvgy64s2t1KeLy8PmLkA6H0p0BE6B8YvFgmNrSAEk+1DUOHop8CAA8p0FM4V39jYGPX09NDY2BjLfKnydXcHrv9ZsyYz+QoLx6in+yiNvVjL9/SB+NLmizS30pbvte9qr9k0KfQ1Co9fLEyNrSAQ42uAqqoOKNNRWHxq+bZtCxRAbW2Zy0eUnC8i4hNfOF2PBK4Dsu3nly+TPkGIAdsCqLMzqA+QiUmhcofibPmC76bgmC9Vvv/6r0ABdPx4ZvIdPx50pwr3RUN8KfcldadSovls+wMFUNcjyftSnS/FPrkLTDAL22uAQi44TfAc8dgYUFcHtLYCNTVAS4v4jPh8Ph+cTifcbh++9S1++VLl06//Oe00YNaszOQbHtbG1ufzsbuGQnzp9/nOfSCw/9Odb/pHgfzJAHmA0X8k70t1vhT79PctU2MrTGyyXYGFE/OzwAz8ZaB6h2Lxpd83f772+i9/mWc+04hPfDr2vxF5Rvnmy4ZPEMJQqwAiijkpcqFDcTZ9o6MeWr7cQfn5Hpb5UuGzW93+018//GHm8slnQU1sn+exfHLsWE4ed5yiJEv5VPaZ/pw9YcKjXgFEFHFS5EqH4mz6VqzQPrF6xYoelvlS4XvpV3f4C6DLLstcvpifBp8Di5D4Yvt6tq/Q9v/2FSzzqeyLObcEIQZqFkBEIZMilzoUZ9NXVOSir3/9MA0NuVjmS4Xvf792q78AymQ+l8tFhw8fJpcrytgqvgiJLzau0SE6/Puvk+vRoszl8/mIho4QjfwjNb5U50uRL+7cEoQosL0IOi5MOgrnlO+6Fjz44Ol886XA1/FfrwAApk4ewhe/PBmPtmQm36RJk3D66afH9QHIiQtTxRfKpOKpOP0LDwK7RzKTz+sGfnsa4OwFzrsduPDu5HypzpdCX9y5JQhRULcAAo+Owrnku6qsEDt21OHyyz+DadOmscuXCt9v/3wlAOATZ/0FG7/TlLF8g4ODeOWVV3D55ZdHH1uFFyHxxca//y9rwrRM5CuYBEwq1QogvSN0Mr5U50uhb3DYGX9uCUIElC2AArcaFwG1Ldri80ErsBumJpn4AN+729FtOxWeSy4BkNgbiQrbu2wZYLfnAQAWfHwko/k8Hg+6u7vh8XhiixVdhMQXG//+9yFz+WYuBBwHjRdA8Xypzpcin+dja43NLUEIJ9vn4MIxcg0Ql47C4lPPp/97bMMYm3wREZ/4kvX9bU2gIeLIMX75UukTBBMoVwBx6iicaz6fx0XuF2vJ91gey3zJ+i66KFAA/e1vmc3n8/nI7XaTz+eL+zOS2V7x8fRF3P/pznd0e6AAOro9eR/T/eF7LC/xuSUIpFgBZOhW6AQmmfhCv++/nXT7Cpb5kvWtWqV9PWUKkb9lSIbymb5VV5FFSHyxibr/05nvxS8HCqAD9yTvY7o//C0G5DZ4IUGUKYC4d+zNBZ/T6aSDBw+Sc2SQZb5kfXrfn098IvP5/GPrdMZ4ofntFR9vX8z9n858m6ckf5qIwfjFwjkyaH5uCRMavgXQ/k7/Yxw7CotPLZ/HQzR1qvbYt7+d3XymEJ/4zPpaQPTbs/jmS4VPEEzAtwDaUEXk5dtROBd9Q0NDtGvXLhoaGmKZLxnfwYOB63+amjKfb2jnMtr1wo7A2JqB+yIkvqhPHTe3MpXv9+dqvk2TiNyO5H0M94ehsRWECPAtgBp5dxTORV9/fz+tXbuW+vv7WeZLxrdpU6AAevXVzOfrf7iU1v70P6i/L8G7cWLk47QIiS+2L+LcykS+oSNEu65RfvxiYXhsBSEMtn2AfHOrWHcUzkVfaWkpbrnlFrb5kvF1nmiFUlAAnH9+5vNZfHW4ZdJ/Awe72PdVEV/qfRHnVibyTT0N+MxTwO46pccvFobHVhDCYFsArd7UiJvOnsG2o7D41PLpBdDHPgYUF2cn36ZbwWbREJ/4ctYnCEbJ9iGocPRTYMABWlbrJu9LyR0eDb6GQ3yxfb29vXT33XdTb28vy3xmfT4f0ezZ2nO+9rXs5CsrOzG2BlsMGILh6QjxRfZFmlsZzedxEln3KTt+sTA1toJAjK8Bqqo6wLqjcC76RkdHad++fTQ6Osoyn1nf++8Hrv/5+c+zk2/p0hNjO+xgsWiIL7O+8LmV0Xxv3Ee0sVD7547f4iLj+ZL0mR5bYcLDtgDq7AzqA2RiUqjUoVh86fX97ukxfwG0axe/fLmwCImPse/QY4Hb4fvb+eVLt08QosC2ABrXCTqBSaFah2IuvuHhYdqzZw/Z7cMs85n13fXNzf4CyGbLTj59bIeHhxP2GUJ8rH3Dg/bQ/Z/JfPa/BQqgd36dvC/V+ZL0jZtbgmAQdQogIkOTQsUOxVx8fX19dN9991FdXR/LfGZ91170JAFE8+aN/6ygTOXTx7avry8hX0KIj62v79m68fs/U/m8nkBH6Ff/OXlfqvMl6Ys4twTBAGoVQEQxJ4WKHYrFl37fGR/qIYDo+iv+zDJfLixC4mPue/YT2mueu4xnvkz5BCEI9QogooiTQtUOxeJLr6+/P3AB9E+qb2eXL5ovKcQnvnD+8m3t+VumakeEuOXLpE8QTqBmAUQUMilU7lDMyTdrVh/dfvt91N1t4lAy0+3duTNQAD3TeE/W8sU9TM990RBfUrq+nm66757bqe/hWdnJ93ZT4DqggYPJ+1KdLwmfnAITzMK2EWJccqRDMSdfftWDuPrqT2H69BKW+cz49AaIALDwhn+D71BnVvKVlJTgU5/6FEpKoowt9+Zy4kvKV3LSdHzqyqtR4uwCjmzOfL6ZCwNfWzuB6eck50t1viR8ceeWIERB3QIIudGhmJ3vk+bfpDhur14AzZkDzJ5ThLrbspOvpKQEl156aWyhwouQ+GJTUlKCSz/5KcB3CbDbm/l8lvOBvAKAfMDIkeR9qc6XhM/Q3BKESGT7EFQ4Rk+B5UKHYm6+0Q3FtO/xb2nN+hjmM+M791zNUVWV3XwJNWtT+HSE+CITsv+zla+/Q2uEmCpfqvOZ9I0OO6QRomAKJQugXOlQzM3Xu30F3X3X7drHNTDMl6hveJgoP1/zfPSj2c2XcLt+xRYh8cVm3P5nlk9lX+/2FfJRGIIplCuAuHfsFR8f35//HLgAmmO+uIhPfOIz5hMEE7D7zYlVAGW7o7D41PLdf3+g+Pn85/nlM4T4xJdq38gxolGDR0tU2V5BMIEyBRCHjsK57uvv76e1a9dSf98xlvkS9ZWXa77CQiKnM7v5/GPb3x/jhebziY+3L+b+z1S+sRGiJ0/RHt9/V/K+VOczSX/fMfNzS5jQ8C2A9nf6H+PesTdXfENDQ7Rr1y4aGhpimc+MDyC67LLs5xty2AJjawbmi5D4YvtC5lY28z09T3vspetS40t1PhPEHVtBiALfAmhDFZGXf8de8fH16RdAf+c7fPIlhfjEl6zv5eu1/3/6TJ75BCGD8C2AGnl1FJ4IPqfTSQcPHiRn8PkiRvkS8VVVBY4ArV+f/XzORyfRwSe/Qc4RA7chG8zHchESX8SnRZxb2ci3/07t6xYQuazJ+xjsD8NjKwhhsG2E6Jtbxaqj8ETwDQwMYPPmzVi1ahXmzJnDLl8ivquuLsLzz2tPWbgwpiEj+WyD38TmV8/AqimrMKfqIfbN5cSXWl/EuZWNfGXdge9ZO4EPfS45X6rzmfAZHltBCCfbFVg4+hGgq6/qpC23Bv4ST+YvDbdba1Ynvtj4fD5yu93k8/lY5kvE96/f9RBAVFRE5HJxyOci94u15Hssj81fzuLLnC/a3MpKPv3fG/elxpfl/ZHw2ArCCdgWQMABVh2FxaeW78qFrxNAVFHBJx+nRUN8E9zXAqJXlvHNJ9cECRmAbQFUVXWATUfhieKzWq20fv16slqtLPMZ9XlfqqXpU+wEEP3TN70s8t1ww4mxPd7Le9EQX1p84XMrq/me+JDm2zJNmfGLhemxFSY8bAugzs6gPkAmJgXnDsVcfQ6Hg7Zt20YOh4NlPqO+d/7u9l8AvfZ7D7LIZ7UGxpbDoiG+zPqC51bW83WuDhwFevl6JcYvFkmNrTChYVsAjesEncCk4N6hWHzp9bW2Bu4A23PnJ9jlS9RnCPGJzyjD7xPZ3yB6uZpnvnT4BCEC6hRARIYmhQodirn6XC4XdXUdptpaF8t8Rn3f/77eB8hHw211LPK5XC46fPgwuYKvyOa+aIgvZT7Xi7V0+N2u0P3PKJ/KvohzSxAMoFYBRBRzUqjUoZij7+jRHrrzzjuprKyHZT6jvquu0p5z7rl88vX0aGPb09Nj2GcK8bH09TxUpu3/7qMs86nsizq3BCEO6hVARBEnhUodivn6PDRtmoNqajxM88X3+XxEZWXa8+rq+OTzeDzkcDjI4/EY8iWF+Nj5PC/VkOORaeRJsqVCSvN5x4gcbysxfrF8MeeWIMRAzQKIKGRSqNShWHzp9X3wQeD6n5/9jF8+Iz4VFyHxKeZ783+INhcTbSoi8jj55Uu3TxCIcSfouCjaoZizr7DuAXz+6jYMD18Ni8XCLp8RX0dH4OuKCj757HY7tm/fjquvjjK2DDvsii91PrtjGNuPfAVXlxXBcmRj9vNNLgO8Tu3rgb8BpRWsxy+WL+7cEoQoqFsAARjzFuHGtS24bi5Qe2krqk8F8gtaAJibZBPdVzW7ELuGvor8/HyW+Yz4OjsDXy9YwCdffn4+iouLY48t80VcfOZ9+fn5KJ5SgvyLfwkcGMt+vtKgvw5sndr/Mx6/WD5Dc0sQIpHtQ1DhGD0FpmqHYvGl1/eVr2ivKS/nmc8Q3E8fiE99n9dDtLlEe82rt/DLl0mfMGFRsgBStUMxd9/Yi7XU81AZjb1YyzKfEd/pp2uvq67mlW9sbIx6enpobGzM2A/hvmiIL6GXj9v/HPI990nt+c9emhpfqvMZ9I29WJvY3BKEEyhXAKncoZi7r6f7qHY76UNlLPPF8/X1BS6A/q//4pXP1K26Ci1C4otNxP2f7Xyv/rP23M1TtCNCyfpSnc+gz99iQG6DFxJEqQKIe8de1X1jY2PU031UOwLEMF883/PPBwqg7dt55Uv4CJCJfOLj64u6/7OZ751fa89rAZH9b8n7Up3PoE+OAAlmUaYA4txBWXw8fI2NgQLo/ff55TON+MSXDl9/R6AAOvQYv3yJ+ATBBHwLoP2d/se4d1DOFd/AwAA9+eSTNDAwwDJfPN/SpZrjlFP45RuwHQ+MrRlUWITEF5WQucUln8el9QFqAdH+HyfvS3U+g8QdW0GIAt8CaEMVkVeVDsq54bPZbLRx40ay2Wws88XzfeQjPgKITj6ZXz7b8zfQxpbHAmNrBsaLkPhi+8bNLS75jr9K5IqRKdv5DGBobAUhAnwLoEb+HXvFx8c38OA0/+kvjvm4LRriE19O+gQhAdg2QvTNrVKmg3Ku+LxeL0ZGRlBSUoKCggJ2+WL5Ojt/4n/o8k950NJSyCrfmAdwHnoWJb46FFzOv7mc+FLrizi3GOVT2Wd4bAUhnGxXYOHoR4CuvqqTttwa+Es8mb8M3G6tWZ34YhPtVm0u+WL5Fi7w+I/+vPv4t9nlu/FribcYiAn3v8TFF/LthNsgZHp7XVai0d7U+VKdLwbyafCCWdgWQMABlh17c9nncrno8OHD5HK5WOaL5wOIZs0YIN9j/PIVFbno8Ltd5DLYYsAQjBYh8cX2RZpbLPJ5x4h+e5b22L4fJO9LdT4DmBpbQSDGBVBV1QFlOiiLL/u+GTO0/1Yu9rLMV1Nz4kEmi4b4xOf3PXOu9v8vfJ5nPkFIE2wLoM7OoD5AJiYFlw7AKvkcDgdt27aNHA4Hy3zRfNdfT1RQoH3d0MAvX00NkdUaGFv2i4b4Uu4Lnlvs8r2yXPv6iTlEPh+/fHF8SY2tMKFhWwCN6wSdwKTg1AFYJZ/VaqX169dTb6+VZb5ovt27A6fANm3il8/tDoyt1WpN2GcI8bH2WY/3hu5/Tvn+cKH23xYQDR/lly+Ob9zcEgSDqFMAERmaFBw7FIsvvb7/+79AAfTWW/zyJeNLCPGJz6xP//fB73nmk9NhQhpQqwAiijkpuHYoVsXncvmottZNeXk+lvmi+Vau1B476SQir5dfPiIin89HbrebfOGnGLgvGuJLic/3WB65X6wlnyfJC3XTke+lrwQKoL/+iF++OL6oc0sQ4qBeAUQUcVJw7lCsim/FCu120hUreljmi+a7+GLt8csv55mPKM6tuoos4uIz7+vZvkLb/9tXsMxHW6ZqvidP4Zkvhk9ugxfMomYBRBQyKTh3KFbJN2mSk77xjYM0OOhkmS+Sz+0mmjxZKzz+3//jl0/H6XTSwYMHyemMMrYKLOLiM49zZJAOPvkNcj46iWU+evn6wFEgjvli+OLOLUGIAttO0HFRpEOxUr5rW9DUdA7ffBF8Bw8CLpf2dUUFv3w6kydPxjnnnBPXB4BNh13xpc43ecpJOOcrTcDuIZb5ULoIeP9JIK+AZ74YvrhzSxCioG4BBGDMW4Qb17bgurlA7aWtqD4VyC9oAWBukk1039VzCvCnV76Fiy+5FFOnTmWXL5KvoyPw/IUL+eXTGR4ext69e3HxxRdHH1vmi7j4zPv8+39RM6YyzId5XwdOvRaYOg/489f55YvhGx51x59bghABZQugsTGgrg5obS0Calu0xeeDVmA3TE0y8QFjh57HG8fPxAXnfzzhN5Jsba9eAE2eDHzsY/zy6bhcLrzxxhu44IILYo8t40VcfOZ9IfufYT6UfBjAhwOv55Yvhs/10V8Zm1uCEE62z8GFY+QaIJU6FIsvvb5Pf1p73sUX88xnGvGJT3zGfYJgAuUKIJU6FIsvvT7vmJtOOkl7bn09v3zsFw3xiS8cn5do8BDffNF8gmACdr85sQogrh2Uc8XX29tLd999N/VuX8EyX7jv7xtv9TdA/L//45cv2Ocf297eqP6IqLQIiS8qUfc/k3xERPT2g0RbTtJe63bwyxfF17t9hbm5JUx4lCmAuHfszQXf6Ogo7du3j0aHHSzzhfs2f6fWXwC9+iq/fME+/9iOjsZ4ofl84uPti7n/GeQjIqIjTwVuhe/5I798URgddpifW8KEhm8BtL/T/xj3Dsriy46v4YanCSAqKPDQ6DC/fJwXDfGJbxxDhwMF0MH/5ZdPEFIM3wJoQxWRV40OyrniGx4epj179tDw8DDLfOEsqfQSQHT+aX9lmS/YN/zCMtqz+5XA2JqB+yIkvqhPHTe3mOUjIu2T4LfO0p6/++v88kXB0NgKQgT4FkCN6nRQzhVfX18f3XfffdTX18cyXzA+H9GsWVphcuPnX2SXL9zX9/Asuu+e26mvp9uEJHI+TouQ+GL7Is4tRvn87KzUnvuHC3jmi4DhsRWEMNj2AfLNrVKng3KO+GbPno3bbruNbb5g3n8f6O/Xvq646nL4Tq1hlS/cV+qrw22T/gv4+9+B2bz7qogv9b6Ic4tRPj8zK4BjO4CBvwFeJ1BQzCtfBAyPrSCEwbYAWr2pETedPUOJDsriy7yvszPw9QUXFqCOWb5Ivk23gs2iIT7xRfSVnvg8GfIC9teBWRfzyicIqSTbh6DC0U+BAQdoWa2bvC8ld3g0+BoO8cX2RTqUzClfMHfcQf47wK69ll++cN+sWSfG9tk6VqcPxJcZn6nTNNnY3oG3AhdCv93EL18E5BSYYBa2BVBV1QF2HXtz3Rd+MSG3fMF88YuaR2+EyC1fuG/ZshNjO2hnsWiIL7M+0xfqZnp7fd5AL6COf+eXLwJyEbRgFrYFUGdnUB8gE5OCewdg8SXn+/CHA0eAOOaTjtHiU9bX92ei0V6++QQhRbAtgMZ1gk5gUnDqAKyST2/W5nCMssyn8/77geLn/PP55YvkG9cIj/uiIb6U+pJu1qfY9mbSl1STUWFCo04BRGRoUnDrAKyST2/Xv2JFL8t8uk//AFSAaNs2fvki+SJ+FEIOLULii+1Lycc1KLS9mfSZ/pgZYcKjVgFEFHNScO8ALL7U+fR/ca97VHh7TSE+8aXK53Ykdios0/kEIUnUK4CIIk4Krh2AxZd636mnav897TSe+VLhSwrxiS8Zn8dF9IcLiVryiF77F375pAgSUoSaBRBRyKTg3AFYJV9paT/9x3+spWPH+lnm033z52uFxle+wjNfJF9/fz+tXbuW+vujjC33RUN8Sen6+47R2p/+B/U/XMoy3zjf9ou0r5//NM98Qb64c0sQosC2EWJcmHYoVtlXcFUzKivPxUknTWaZD7Ut+OX9RZgzR3tKRQW/fNF8kydPxrnnnovJk6OMLffmcuJLyjd5ykk4d1ElJtN7wJHN7PKN851Urn1t6wTIB+Tl88oX5Is7twQhCuoWQODZoVh53+Xm36Qyke/l1zdC/7VduJBfvmi+qVOn4sorr4wtZL6Ii8+8b+rUqbjys4sB32eA3V52+aL6PEPAYBcw/SM8813WYmxuCUIksn0IKhyjp8C4dihW2ed8dBIdfPIb5BwZZJmPWkA//85v/BdAf/ABv3zRfE6nkw4ePEhOpzO+nPnpHPEl7gvZ/wzzjfPtWKz5WkD07mP88gX5nCODxueWIAShZAHEuUOxyr6e7SvozjvvpJ7tK1jmoz/W0IpPbSCAaM4cH/l8/PJF8/X09Ghj29Nj7IeosEiKzzDj9j+zfONwDWgXQbeA6Hfn8MsX5PO/bxmdW4JwAuUKIO4de1X2+Twucr9YS77H8ljmI6+bzj3zCAFEV13SyTJfNJ/P5yO3200+o1WbyXzi4+mLuP8Z5YvIMx8PHAXimO+Ez/dYXuJzSxBIsQKIawdl8WXGNzxMlJ/vI4Do+1/+L3b5EvUZQnziy5Zv942aZ9MknvmCfYJgAmUKIM4dlHPFZ7Vaaf369WQ93ssy3549gQaIj//4Z+zyxfL5x9ZqjfFC8/nEx9sXc/8zyBeRN39xogAqInrxS/zyncB6vNf83BImNHwLoP2d/se4d+zNFZ/D4aBt27aRw+FgmW/t2kAB9M7f+eWL5XPYrYGxNQPXRVJ8hnwhc4thvoiM/IPI2qk1RuSY7wRxx1YQosC3ANpQReTl37FXfJnzfetb2vNmzCDtAmhm+Yz4kkJ84hOfIKQMvgVQozodlHPF53K56PDhw+RyuVjmq6jQCpErrkiNL9X5YvlcjxbR4d9/nVyjQyYkkfOxXITEF/FpEecWo3wq+wyPrSCEwbYRom9ulTIdlHPFZ7fb8fDDD2PVqlWYo7dbZpLP7QYOHNC+DukAzSRfPJ918Jt4+NV5WFX4bcypeoh38zvxpdwXcW4xyhcTImDkfWDq6SzzGR5bQQgn2xVYOPoRoKuv6qQttwb+Ek/mLw23W2tWJ77YeDwecjgc5PF42OXr7Axc//Poo8n7Up0vvm+UHDuWk+exfDZ/OYsvc75oc4tLvqi8u5Godab2utGgPjtc8pGJsRWEE7AtgIADynRQFl/6fevXBwqg4BsEueQz4uO0aIhPfIZ8/3hee34LiI4+yy+fICQB2wKoqupAVjr2TmSfzWajjRs3ks1mY5fvO9/RXjdlCtHYWGq2N5X54vluuOHE2Pb38V40xJcWX/jc4pYvKqN9gQLowD388lESYytMeNgWQJ2dQX/mm5gUXDoAq+QbGBigJ598kgYGBtjlu+wy7bWXXpq67U1lvni+48cDY8th0RBfZn3Bc4tjvpg8dVrguQzzJTW2woSGbQE0rhN0ApOCewdg8SXm83iISkq0169axS9fun2GEJ/40uV76Sva8347n2c+QTCJOgUQkaFJwa0DsEq+sbExOnq0h2prx1jle/PNwPU///d/fMcvlm9sbIx6enpoTD9/Z9CXEOJj6xt7sZZ6uo+G7n9G+WL69t8ZOA3msrPLF3FuCYIB1CqAiGJOCo4dgFXyHT2qfWJ1WVkPq3wtLYECqLKS7/jF8kX9NHiFFnHxmff1PFSm7f/uoyzzxfS9/7tAAXTsRXb5os4tQYiDegUQUcRJwb0DsAq+2toxKivTjgBxynfb9zwEEOXl8R6/WL6Yf6UqsoiLz7xv7MVa6nmojMZerGWZL6Zv+INAAfTmz9nlkyNAglnULICIQiYF5w7A4kve97mK/f4jQBzzpdrHdREX3wT1+XxEW8u07/3l2/zyCYJJ2HaCjosiHYBV8hXesA7XXv8yRkcXo6hoOot8Xi/QcfBUAMC8eT60tOSzHb9YPofDgZ07d2Lx4sWYPj3C2DLssCu+1PkcQ6PY2bsUi+cUYvqRTezyxfTl5QFX/A4oOQ2Ycgq7fHHnliBEQd0CCMCYtwg3rm3BdXOB2ktbUX0qkF/QAsDcJJvovsrZRXju+Bfg8/nY5Lt+TQvsI9rr//XLD6Oo4MakfNnaHz6fD06nM/bYMl/ExWfe5/P54HS54av4JfCGh12+uL7Zl7LNZ2huCUIksn0IKhyjp8BU6gAsvuR8+r8//eiTLPOlwhcC99MH4hNfLvmECYuSBZBKHYBV8nleqiHHI9PIk+BnX6Uz38c+RicugPbR4HMrWI9fLJ+ynwUlvpT4xu1/ZvkM+zyjWndoRvk8L9XIZ4EJplCuAFKtA7BKvp7uo9rtpA+Vscl39dXa1x/9aOq3N5M+U7fqqrpIim8cEfc/o3yGfM99imhjAdGfb2aVz99iQG6DFxJEqQKIe8de1X0ul4sOv9tFrhdr2eT70If0z9JK/fZm0udyuejw4cPkcrmi+iOi2iIpvohE3f9M8hny6XeCbV/EKp/rxVpzc0uY8ChTAHHuoCy+9Pj+8Y/A9T8//Sm/fMn4EkJ84uPiawHRpknJOdORTxBMwLcA2t/pf4x7B+Vc8TkcDtq2bRs5HA4W+f7wh0ABtHNn6rc3kz6H3RoYWzOotEiKbxwhc4thPkO+Zy8JFEHH97LJF3dsBSEKfAugDVVEXjU6KOeKz2q10vr168lqtbLI95OfBAogPVIqtzeTPuvzN9D69b8OjK0ZVFgkxReRcXOLWT5DWDsDBdD2i9jkMzS2ghABvgVQI/+OveJLr++667TC48wzeeZL1MdyUROf+Ay7xog2TwkUQdzyCUKCsG2E6JtbpUwH5VzxERE8Hg8KCwuRl5eX9Xydndp/KyrSs72Z9Hm9gO+9p1BIdcj7lCLN78SXMl/EucUonzFXIWC5EOj/MzB5Fpt8hsdWEMJgWwCt3tSIm86eoUQH5Vzx9fX1Yd26dVi1ahXmzJmT1XxWK/Dee9rXMQugLOVL1Pet9b9A+fzzsGpsLebk1WV90RBfZn3R5haXfIYpXagVQF4ncFo1i3wJja0gBJPlI1Dj0E+BAQdYduzNZZ/T6aSDBw+S0+nMer4dOwLX//zhD+nZ3kz6Jk1y0sE3/0bOXUv5nj4QX9p8keYWp3yGeftB7fUb84kGDrLIZ2psBYEYXwNUVXVAmQ7K4ku976c/DRRA3d388pnxESXni4j4xJdJ38gxor4/E40N88wnCAnAtgDq7AzqA2RiUnDpAKySb2hoiHbt2kVDQ0NZz7d8ufbck09O3/Zm0mezBcaW/aIhvpT7gucWx3wq+5IaW2FCw7YAGtcJOoFJwakDsEq+/v5+Wrt2LR071p/1fB/9qI8Aoi98IX3bm0mfPrb9/f0J+wwhPta+/r5jofufWT6VfePmliAYRJ0CiMjQpODeAVh88X2D66dSXp6XAKIf/pBfvkz6EkJ84suUz+cjGj6q/ZdjPkEwgFoFEFHMScGxA7D4Eve9svaH/ut/ntw6xi5fpnymEJ/40u17/7dET8zVXjP4Lr98gmAQ9QogooiTgmsHYJV8K1b00u23300rVvRmNd+vfunxF0DvPn6LMuMXy9fb20t333039fb2GvIlhfjY+Xq3r6C777qderevYJkvIV/vn7TntoDoyBNZzxdzbglCDNQsgIhCJgX3DsCq+IqLR+lb39pHDsdoVvN94xtaITFz2iD5HlNn/GL5RkdHad++fTQ6GmVss72oiS+tvtFhB+17/Fs0uqGYZb6EfO5BopY87bn7fhj5ORnMF3duCUIU2DZCjIsiHYCV8n25BQ88cGHW83V0aP9deHEJ6LQadcYvhq+4uBgXXnhhXB8AFs3lxJdaX3HJNFz41QeA3QMs8yXkKzoJmH4O4DgI2Dqyni/u3BKEKKhbAEGNDsAq+b4wtwDtr30HF1y4ECUlJVnJ53IBf/ub9vWCBfmoU2j8YvlGRkawf/9+XHDBBdHHlvkiLj7zPv/+X/AgShjmS9g3c6FWAFmjFEAZzDfiHIs/twQhAsoWQGNjQF0d0NpaBNS2aIvPB63AbpiaZOIDnIfa8Kfu+Thr/vyE30hSle/11wGPR/v6T38C/vIXdcYvlm9kZAR/+tOfcNZZZ8UeW8aLuPjM+0L2P8N8CftKK4DDmwDnMWC0G5hyctbyjZz9S2NzSxDCyfY5uHCMXAOkWgdg8Rn3NTUFOkBzzJcOX0TEJz7OvmMvBC6E/uCZ7OcTBBMoVwCp1gFYfIn5br45UPxUV/PLl2pfTMQnPq4+ly1QAO2/K/v5BMEE7H5zYhVAKnYAVsnX19dH9913H/U9W5e1fDNnar7Zs9Ubv1g+/9j29UX1R0TlRVJ8fqLufyb5TPl+O//E40uzmq/v2Tpzc0uY8ChTAHHv2JsLvuHhYdqzZw8ND9qzku+rXw0c/bn11vRvbyZ9/rEdHo7xQvP5xMfbF3P/M8hnyte3h2jwUGg36CzkGx60m59bwoSGbwG0v9P/GPeOveJLnU//t2EDz3zS4Vl84mPoEwQT8C2ANlQRedXvAKySb1xDsQznu+iiQAEUrw9mNvIl4xt9YSnt62hPrlkb90VIfFGfaqhZXw5tbyZ90ghRMAvfAqgxNzoAq+SL2FI+g/lWrdIKjSlTiMbG4qsynS8ZX+9DZXT3j++g3mNHTUgi5+O0CIkvts/wxzWouL0eF9FonO1KYz75KAzBLGz7APnmVuVEB2CVfGVlZbj99tuzlu+KK7SnLFgAFBr9zWQ0frF8s3x1uH3yj4G33wTKFOz7Ir6kfBHnFqN8pn19fwJcx4EPfwn49Nas5DM8toIQBtsCaPWmRtx09gzlOwCLz5iP0IJ9+zRfRQW/fKnwbboVaixq4hNfor5YHaEznU8QjJLtQ1Dh6KfAgAO0rNZN3peSOzwafA2H+GL7+vv7ae3atdTf35/xfAc2/Kv/+p/16zOzvZn0lZb209r776f+54y1GDCEiqdLJqgv0tzilM+075nzAv2ARnuyks/U2AoCMb4GqKrqALuOvbnuGxoaol27dtHQ0FDG82349gp/AbRvX2a2N5O+ZctOjK3Dxn9RE1/KfeFzi1s+07z/20AB1PaZrOQzPbbChIdtAdTZGXQbkIlJwb0DsPhCfd+teYYAoklFbnI7+eXLti8m4hNftnwj3YECiGM+QYgB2wJoXCfoBCYFpw7AKvmcTicdPHiQBgedGc/36U97tVvhy19Vdvxi+fSxdTqdCfsMIT7WPufIYOj+Z5YvKd+Tp2iep07LSr5xc0sQDKJOAURkaFJw6wCskq+np4fuvPNOWrGiJ6P5vF6ik07SnlP/5eeVHb9YPn1se3p6EvIlhPjY+nq2rxi//xnlS8q364ua43dnZSVfxLklCAZQqwAiijkpuHcA5u5zuXxUW+umvDxfRvO99VagAWLTujFlxy+Wz+fzkdvtJl/4xwZwXdTEl1Kf77E8cr9YSz6Pi2W+pHx/vSNwCsx5POP5os4tQYiDegUQUcRJwbUDsPji+zZuDBRAe/fyy5cNX1KIT3yZ9AVfCN23h18+QYiCmgUQUcik4NwBWCWfxWKlf//39dTba81ovn/7N61wKCwk8nezV3D8YvmsViutX7+erNYoY8t90RBfUjrr8V5a/z//TtbfWFjmS8rnshH1vETkHshKvrhzSxCiwLYRYlwU6QCskq/gC0244oqTUVxs4tciiXwdJ3qoffzjQHFx8r5U50uFr7CwECeffDIKo7W4VrH5nfgMUzipGCefcwUKJ30AHNnELl9SvkkWYM5nspYv7twShCgo/RujSgdgpXyXm3+TMpOPKFAAhXeAVnL8ovimTZuGq6++OraQ06ImvpT6tP1/DeCrAnZ72OVT2WdobglCJLJ9CCoco6fAVOkArJLP9WgRHf7918k1mnhDMbP5uroC1//cf39mtzeTPpfLRYcPHyaXy8BFsJxOb4gvJb6Q/c8wX8p8o33ah6NmMJ9rdMj43BKEIJQsgFTqAKySz3+r7vYVGcvX2hoogPbsyez2ZtKX8K26nBY18SXtG7f/meVL2te7O9AHqPeVjOZLSYsBYUKiXAHEvWOvyj6Pe5QcO5aT57H8jOX7j//QXpufTzQ8rPb4xfJ5PB5yOBzk8Xji/oxk8omPpy/i/meUL2nf4LuBO8EO/jKj+TyP5Sc+twSBFCuAVOwALL7Yvqoq7fXnncczXzp9hhCf+FTw+XxEraXa8/bclPl8gmACZQogVTsAq+Sz2Wy0ceNGsvX3ZSSfz0c0e7bmWLFC/fGL5fOPrc0W44Xm84mPty/m/meQLyW+nZXac/5wfkbz2fr7zM8tYULDtwDa3+l/TOUOwCr5BgYG6Mknn6SBgYGM5Dt8OHD9z4IF6o9fLN+A7XhgbM2QK4vkBPWFzC2G+VLi62jQvr+xgGhsJGP54o6tIESBbwG0oYrIy79jr/jM+556YsxfAHHMlw5fUohPfJx9720JXAfU95fM5xOEBOFbADXmRgdglXxjY2PU09NDY2NjGcn3wxtb/cXPtdeqP36xfGOPFlLP9hU05orzl3EC+ZRcJCeoL+LcYpQvJT7H24EC6O8PZCyf4bEVhDDYNkL0za3KiQ7AKvmsVivWrVuHVatWYc6cOWnP17F/CgBg2jTC44/nKT9+sXzHB7+Jpr+chVWox5yqh9g3lxNfan0R5xajfCnxnVQOFE0HxhyAtSNj+QyPrSCEk+0KLBz9CNDVV3XSllsDf4kn85eL2601qxNfbKL9JZWufJYSq9ZIcPEfc2L8YvmWLx3RjgA9WqjeX/biS9qX8FEKVbe37Urtsecuy1g+OQIkmIVtAQQcyIkOwOKL7tP/3XfD91jmS7VP2UVNfOIz6uvvIBo4SOTzZj+fIMSBbQFUVXUgJzoAq+QLv5sinfk+9alAAfTC/96ZE+MXy7d8+YmxtR1Xb1ETX9I+03cqKbq9mfTJXWCCWdgWQJ2dQX2ATEwKLh2AVfIF9ypJd7477ggUQLb+3Bi/WL6+vqA+MAwWDfFl1id9oNLnS2pshQkN2wJoXCfoBCYF9w7A4iP6yle0/58/n2c+Tj5DiE98nHxjo0Qjx/jkE4QIqFMAERmaFNw6AKvk83g81N/voJoaT9rznXZa4LFsbW8mfTn/WVDii+nzvFRDDnt/cp9XpdD20sYComcvyUg+U5+zJwikWgFEFHNScOwArJLv6FHtE6vLynrSmq+3N3D66957s7e9mfRF/TR4lRY18Zn29TxUpu3/7qMs86XU99uzThRB+fE7QqcgX9S5JQhxUK8AIoo4Kbh3AFbBV1vrotNPP0y1ta605nv22UAB9Pzz2dveTPpcLhcdPnyYXC6XIV9SiI+dz/ViLR3+9enkerGWZb6U+t7+teZqAdHOqrTnizm3BCEGahZARCGTgnMHYPGN991zT6AA6uvjly/bPpaLmvjEZxTb/kABxDGfIJyAbSfouCjSAVglX8ENTVh+w5/hdF6OoqJpacvX3q799/TTgdmzk/elOl86fIODg3jllVdw+eWXY9q0CGOrcgdg8cVlcNiJVwa/jsvnFmLakU3s8qXUN/1jQEEx4HUCJ52V9nxx55YgREHdAgjAmLcIN65twXVzgdpLW1F9KpBf0ALA3CSb6L7Fs4rw26Ofg8fjSWu+jhNd8hctSo0v1fnS4fN4POju7o49tpwXNfEl5dP2fw88X/lf4E0Pu3wp9eUXApYLgf6/AMVzgNKFac1naG4JQiSyfQgqHKOnwFTqACy+gM9qDZz++slP+OXLli8Ezqc3xCc+I7y6Snv95hLtlnhu+QSBFL0GSKUOwCr5fC/XkPvRQvK9nL58O3YECqA//CG725tJn8/nI7fbTT6fz9gP4bioic+0b9z+Z5Yv5b63HwxcA2R/I635fC/XJDa3BOEEyhVAqnUAVsnX031Uu530obK05VuzJlAAdXdnd3sz6TN1qy63RU18pn0R9z+jfCn39XcECqDDrWnN528xILfBCwmiVAHEvWOv6j6n00kH3/wbOXctTVu+ZUu9BBCdckr2tzeTPqfTSQcPHiSn0xnVHxFOi5r4TPui7n8m+VLu87iIutuInP1pz+fctdTc3BImPMoUQKp2ABZfqO8jpx4lgOiLX+SZL1O+hBCf+MQX2ycIJuBbAO3v9D+mcgdglXxDQ0O0a9cuGhoaSku+gWdv9J/+uuM/A23rc2X8YvmGHLbA2JpBhUVIfFEJmVsM86nsizu2ghAFvgXQBq2DqOodgFXy9ff309q1a6m/vz8t+V7aNeYvgH57739nfXsz6et/ro7W3n9/YGzNwHgREl9s37i5xSxfWn0uO9HYcNryGRpbQYgA3wKokX/HXvElpvr5zwMXQL//yw+zy5duX04tauITXzzfYBfRb+drz31vc2byCUICsG2E6JtblRMdgMUXQG+AWFZGOPmCy9jlS7uPU7M68Ykv3b4ppwDD72lfW9uBM5amP58gJADbAmj1pkbcdPYM5TsAq+Tr6+tDc3Mz6uvrUVZWlvJ8egFUUZGHFQ9kf3sz6fvWg7/A2Wd/DPWudShDnbqLmvhM+aLNLS750uIrKAZmnAfY9wPWjrTlS2hsBSGYbB+CCkc/BQYcYNmxN5d9o6OjtG/fPhodHU15vqEhovx8zfPRj/LY3kz6iotHaV9HO42+YKzFgCFy6XRJjvsizS1O+dLm2/MN7futM4niNSo0mc/U2AoCMb4GqKrqQE50ABafxp/+FLj+h2O+TPiIkvNFRHzi4+w7+Cvtey0gGjyU+XyCEAO2BVBnZ1AfIBOTgksHYJV8w8PDtGfPHhoeHk55vl/8IlD8fOELPLY3kz67PTC2yi9q4kvYFzy3OOZLm693d6AAOrw1LfmSGlthQsO2ABrXCTqBScGpA7BKvr6+Prrvvvuou7sv5fnOPFPzFRURuVw8tjeTPn1s+/r6EvYZQnysfX093aH7n1m+tPnGhog25muPd34/LfnGzS1BMIg6BRCRoUnBvQPwRPYBRJ/7HN98HH0JIT7xcfQ9c6722AtXZTefIIShVgFEFHNScOwALD7Nl5en/behgWc+jj5TiE983Hx/+pr2/0+dlv18ghCEegUQUcRJwbUDsEq+uro+uu22+6iuri+l+RYvDhwB2hylH1o2tjeTvpiH6VVc1MSXkK/v2Tq67ye3Ud+zdSzzpdVn3Udk22/eGyefnAITzKJmAUQUMim4dwBWxTdlyjCtXLmH7PbhlOa7//5AAfT3vyfv4zp+sXxxL9RUbVETX0IMD9ppT8tKGt4whWU+lX1yEbRgFraNEOOiUgdgVXxfasGvfnVpyvP98z9rT5k+HZg/P3kf2/GL4SspKcGll14a1wdA7eZ34otIyUkzcOmyXwG7rSzzqeyLO7cEIQrqFkBQowOwSr4vzs3HGwcacM7HzkVxcXHK8rW3a/kWLgTy85P3cR2/WD6n04m33noL55xzTvSxVXgREl9s/Pu/Yj2KGebLiI8IWPQ/QMmpKc3ndHvjzy1BiICyBdDYGFBXB7S2FgG1Ldri80ErsBumJq34gOGuF/DM7nNwyikfQnHxKSnJ536pAK+/vhFAHioqeG1vJn2Dg4N45plncMopp8R+k1ZtUROfIfz7v74exQzzpd038Abw/lbgH88ANY6U5hv8yC+MzS1BCCfb5+DCMXINkGodgCeyr+O/Fviv/3nsMX75OPgiIj7x5ZLvwL2Bhogvfin1+QTBBMoVQKp1AJ7ovgcb1vkLoDcO8MuXbV9MxCe+XPF17wgUQOnIJwgmYPebE6sAUrEDsEq+/v5+Wnv//dT/XF3K8q36tocAoqmTB8nzYi2r7c2kr7+/n9auXUv9/f1R/RHhvKiJz7Av6v5nki/tPpc1UPw8c15K8/U/V2dubgkTHmUKIO4de3PBNzQ0RLt27aIhhy1l+T7xCe05nzr/TXbbm0mff2yHhmK80Hw+8fH2xdz/DPJlxPf0vBOnwL6c0nxDDpv5uSVMaPgWQPs7/Y9x79grvsiKsTGi4mLtebd+x8MuX7Z8phCf+FT3vfxV7fVPnZ76fIJgAr4F0IYqIq/6HYBV8jmdTjp48CA5nc6U+PbvDzRAfPhhftubSZ9z11I6+ObfAmNrBo6LmvgM+cbNLWb5MuI7cE/gNNhoX8ryGRpbQYgA3wKoMTc6AKvk6+npoTvvvJN6enpS4vvNbwIF0F//ym97M+nreahMG9vuoyYkkfOxWNTEZ8gXcW4xypcR39FnAwXQP55LWT7DYysIYbDtA+SbW5UTHYBV8pWVleEHP/gBCguDfi2S8HV0aP8tLgbOPZff9mbSN8tbhx8U3Y3Ct/8GzGHYp0V8afVFnFuM8mXEVxrUCGzgDeDkqpTkMzy2ghAG29+Y1ZsacdPZM5TvAKySLy8vD0URVnmzvvZ27b8XXAAEvzdx2d6M+h5owaZb67Q3eZPNF/1wWtTEZ8gXbW5xyZcRX3EZ8NnnAcv5wJQPpSxfQmMrCMFk+xBUOPopMOAALat1k/el5A6PBl/DIb7YPqvVSuvXryer1Zq0z+MhmjpVe923v81zezPps1istH79r8n6/A08TkeIL6O+SHOLUz6VfabGVhCI8TVAVVUH2HXszXWfw+Ggbdu2kcPhSNr35puB638efJDn9mbSt3z5ibG1W1ksGuLLrC98bnHLp7LP9NgKEx62BVBnZ1AfIBOTgnsH4Fz3PfZYoABqb+eXTzVfTMQnPtV8Y0NE7oHM5BOEKLAtgMZ1gk5gUnDqAKySz+Vy0eHDh2loyJW073vf015fVEQ0OMhzezPp08fW5XIl7DOE+Fj7XKNDofufWb6M+dwOomfOJdqYT/TGT1OSb9zcEgSDqFMAERmaFNw6AKvk028nXbGiJ2nflVdqmRYu5Lu9mfTFazGg9KImvri+nu0rkr9VW6Htjel78mTtOa8sS4lPboMXzKJWAUQUc1Jw7wDM3Tc66qHlyx2Un+9Jyuf1Ek2fruWaN4/v9mbS5/F4yOFwkMfjMewzhfhY+jyP5ZNjx3LyuEdZ5suob9c12vd/d3ZKfFHnliDEQb0CiCjipODaAXgi+t5+y+2//odjPhV8SSE+8XH2/fUO7XstiH4dUDrzCcIJ1CyAiEImBecOwCr5Zsyw0W23baS+PltSvk0/+rm/+Fm8mO/2ZtJns9lo48aNZLPZ4vqUXNTEFxNbfx9tvP82sv1mBst8GfW9/3SgAOp5KWlf3LklCFFg2wgxLop0AFbJV3jNOnzyk8WYNCk/KV/nJjcAIC+P8OSTeWy3N5O+/Px8FBcXIz8/ythyaVYnvrT48gsnofjkTyJ/+j+AI5vY5cuob2ZQR2hrBzDnM0n54s4tQYiCugUQFOkArJrvcvNverrvb399EwDw8XlHMP2kU1Kbj/v4RfFNnz4d1113XWyhyoua+GIyffp0XHf9VwHfl4HdHnb5MuorORWYXAa4+rQCKEmfobklCJHI9iGocIyeAlOlA7BKvrFHC6ln+woac40k5Zs0yUcA0U2feYj19mbSNzY2Rj09PTQ2NhZfrvLpDfFFJGT/M8yXcd8LV2n//8y5SfvGXCPG55YgBKFkAaRSB2CVfP5bdbevMO37whcCFz//8l/Ws97eTPoSvlVXxUVNfFEZt/+Z5cu4r6NB+3rTZCKPiTvjgnwpaTEgTEiUK4C4d+xV2TfmGtGOAD1aaNq3ZUugAHrl5THW25tJX0JHgJLIJz6evoj7n1G+jPt2XU3Ut4fI40zaN/ZooRwBEkyhVAGkYgfgiea7/Xbt//PyiIaG+OXj5DOE+MQnvvg+QTCBMgWQqh2AVfINDAzQk08+SQO246Z9n/+89tjHPsZ/ezPp84/tgIG+JybyiY+3L+b+Z5BPZd+A7bj5uSVMaPgWQPs7/Y+p3AFYJV9IPw0TPp+PqKxMe7yujv/2ZtJn6+9LrlcJ80VIfLF90gcqhu/FLyXlkz5Agln4FkAbqoi8/Dv2ii/w9CNHAtf//Pzn/PJx8CWF+MSXS76//x/Rpkma7+Xrk/cJQoLwLYAac6MDsEq+iJ+pk4DvqacCBdBL0Rq8MtreTPrks6Amts/w51XlyPYa4r3Nmkf/Z9InnwUmmIVtI0Tf3Kqc6ACskq+/vx/r1q3DqlWrMGfOnIR97e3af/PygIUL+W9vJn19g5PR9MpZWDV2M+ZUPcS/WZ34UuqLOLcY5cuKL7gj9MwK0z7DYysI4WS7AgtHPwJ09VWdtOXWwF/iyfyl4XZrzerEFxuXy0WHDx8ml8tlynf11drRn3POSU8+lX3Llw7R4d9/nVyPFvH7S1x8afdFm1tc8mXF5/MSPT5dc+z5J9O+hMdWEE7AtgACDuREB+CJ4vP5iObM0Z53ww388nHwsVyExCe+bPrartBev31R6vMJQhzYFkBVVQdyogOwSj6Hw0Hbtm0jh8ORsO/99wPX//zsZ2psbyZ9y5efGFu7ld8iJL60+8LnFrd8WfO99q/aazdNIvK4TPlMj60w4WFbAHV2BvUBMjEpuHQAVslntVpp/fr1ZLVaE/Y9/XSgAHrxRTW2N5O+3t7A2LJbhMSXdl/w3OKYL2u+Q48GLoLubzflS2pshQkN2wJoXCfoBCYF9w7Auej7zx96/AVQeD8yDvlU9hlCfOJT0Wd/I1AAvd2cvnyCEAF1CiAiQ5OCWwdglXw+n4+Gh91UU+NL2PeFS9sJIDr7bHW2N5M+n89HbrebfD5fQr6EEB9bn+/lGnI7h0P3P6N8WfN5PURbpmqvee27pnwR55YgGECtAogo5qTg2AFYJd/Ro9onVpeV9STk871cQ3NndGvXuizzKrO9mfRF/TR4DouQ+NLu63moTNv/3UdZ5suq79guoqHD2p0UJnxR55YgxEG9Aogo4qTg3gFYBd/SpU46++yDtHSpMyHfB0fc/tNf9/3zI8psbyZ9TqeTDh48SE5nhE+/5rAIiS+tPueupXSw6Wxy7lrKMp/KvphzSxBioGYBRBQyKTh3AJ4Ivt/9LnAB9K7br2CXTzWfiouQ+MSnlE8QiHEn6Lgo0gFYJV/B8mZ845/2we2+GEVFUw2r9A7QAHDhZWcg/4MNSmxvJn3Dw8PYu3cvLr74YkydGmFsOXfsFV/SvuFRN/aOrcTFHyrA1COb2eVj4fO6AM8wMLk0IV/cuSUIUVC3AAIw5i3CjWtbcN1coPbSVlSfCuQXtAAwN2knuu9zpZPw+MFPYuHCCxJ6I9ELoLPOAm555Ne4bu6oEtubSZ/L5cIbb7yBCy6IMbYcFiHxpcXncrnwxptv4YLaX2DqJC+7fFn1EQE7PgMc/zNw1s3AxQ8k5DM0twQhEtk+BBWO0VNgKnUAznXfySdrntNO45lPBV8I3E8fiE98qfY9d5n2/WcvyXw+YcKiZAGkUgfgXPf94x+B63845lPBFxHxiW8i+fbeqn1v02RjPyvcJwgmUK4AUq0DsEq+3mNH6e4f30G9D5UZ9j31VKD4ueIKtbY3k77e3l66++67qbe3N+7PSCaf+Hj6Iu5/Rvmy7ut6RHu8BUTWfQn5eh8qS3xuCQIpVgBx79irum90dJT2dbTT6AtLDfvOPTdQAPX1qbW9mfSNjo7Svn37aHR0NKo/IiovauLzE3X/M8mXdZ/tQKAAemd9Qr7RF5aam1vChEeZAkjVDsATwQcQnXUW33wcfQkhPvHlus/rIdpcoj326i2J+wTBBHwLoP2d/sdU7gCskm94eJj27NlDw8PDhn3Fxdp/ly5Vb3sz6RsetAfG1gwqLmri8xMytxjmY+FL5ELoIOKOrSBEgW8BtKGKyKt+B2CVfH19fXTfffdRn34uK47vS18KHAFas0a97c2kr+/ZutCxNYOKi5r4iCjC3GKWj4Xv1X9O7ELoExgaW0GIAN8CqJF/x96J7nv66UABtHMnv3zcfEosQuITX7Z82y/W/rsxn8j+ZnJeQTAA20aIvrlVOdEBOJd9994beFpFBb987HwqN6sTn/gy4Zv7WeDTTwKTLOZ8gpAI2a7AwtGPAF19VSdtuTXwl3gyf2m43VqzOvHFJtqh5Gi+L39ZO/ozf35m8qnsu3FFN913z+3U9/As/n+Jiy/lvoRP0yi+vZn0ySkwwSxsCyDgAMuOvbnsi3QxYSzfhz/s858eUnF7M+mbMmWY9ux+hYZfWMZi0RBfZn2mLtRVeHsz6ZOLoAWzsC2AqqoOsOvYK76A79gDc/zX//z3f/PLx9FHlJwvIuITX676Xq5O3icIMWBbAHV2BvUBMjHJuHQAVskX3Kwtnu8Pa+7xF0Btz40pub2Z9DkcQY3wVFqExJcSn+lGmBnKx8bX3Ub0xMmay6AvqbEVJjRsC6BxnaATmGScOgCr5NPb9R892hvX9+O7PP4CqP8PNym5vZn0jfsoBM6LkPhS7us9djS5j2tQbHtN+2z7A8XP9osM+Ux9zIwgkEoFEJGhSca9A3Cu+L7yFe05804+xjJfrvgSQnziU93nHSPaPEVzvLoqtfkEIQy1CiCimJOMYwfgXPWdeqr2vOqvelnmywWfKcQnPtV9z16qvf7ZS1OfTxCCUK8AIoo4Kbh2AFbJV1fXT7fcspbq6vpj+np6Ag0Q771X3e3NpK+/v5/Wrl1L/f39hnxJIT52vv7n6mjtPbdQ/3N1LPOx8ukdoTdP0Y4IxfHFnFuCEAM1CyCikEnBvQOwKr6pU4follt2kc02FPOl27YFCqDnn89cPpV9Q0NDtGvXLhoaijK23BYh8aXUN+Sw0a5HbqGhR6ayzMfK985DgeuAbPvj+uLOLUGIAttO0HFRqQOwKr5rWvCLX1wZ19feHvja3wFaxe3NoG/q1Km48sor4/oAqNGxV3wJMXWaBVeu+AWwu49lPla+0kWBr63tgOX8mL64c0sQoqBuAQRgzFuEG9e24Lq5QO2lrag+FcgvaAFgbtJOdN+XPpSPQ1134Mx58zF58uSor9MLoDPPBGbNylw+lX0ulwvvvfcezjzzzOhjy2kREl9Kff79v+ghTGaYj5VvxrlAQTHgdWoFUPlNMX2uMV/8uSUIkcj2IahwjJ4CU6UDsEq+nofK6M4776Se7qMxX3vaadrrvvrVzOZT2dfT06ONbU9PfDmn0xHiS4kvZP8zzMfO9+wntOfvWBzX19N91PjcEoQglCyAVOsArIrP93INuR8tJN/L0X29vYHrf+65R+3tzaTP5/OR2+0mn89n7IdwWITElzLfuP3PLB87X/9rRAN/J/J54/p8L9ckNrcE4QTKFUCqdQDONd/27YEC6Lnn+OVTzRcT8YlPfMZ8gmACdr85sQogFTsAq+SzWq20fv2vyfr8DVF9d98dKIC6u9Xe3kz6tLFdT1arNao/IiotQuKLStT9zySfyj7r8zeYm1vChEeZAoh7x95c8DkcDtq2bRs57Naovuuu0xxnnKH+9mbS5x9bhyPGC83nEx9vX8z9zyAfW5/XTTTSHVPnsFvNzy1hQsO3ANrf6X+Me8feieQ7/XTN8+EP88yngs8U4hPfRPP9+Z+INhcTPXdZcj9LEKLAtwDaUEXkVb8DsEo+l8tFhw8fJpfLFdHX1+P2n/7Khe3NpM/1Yi0dfrcrMLZmyIVFbYL6xs0tZvlY+l5dpX1vcwmR1xNVY2hsBSECfAugxtzoAKySL+Kt2kG+bT+921/8fPrT6m9vJn1GWwwkkk/JRW2C+gy3QciR7U2J751fa4+3gMgW/a7ghFpMCEIQbBsh+uZW5UQHYJV8s2bNwve+9z2UlJRE9HX+dtj/8JYtUH57M+mb6anD9wp+hpK//w2YkwPN6sSXkC/i3GKUj6VvXEfo8yIqDI+tIITBtgBavakRN509Q/kOwCr5CgoKMG3atKi+Q+0dAIDT5/bh5LmWjOdT2reuBZturdPe5HdDnUVIfCnxRZtbXPKx9M04D8ifDPhcJzpC3xjx5QmNrSAEk+1DUOHop8CAAznRAVgln81mo40bN5LNZovoKynxEUB03UVP5MT2ZtI3Y4aNNrY8RrYYLQYSRuXTGxPMF2luccrH1rf9Yu3/n7886stMja0gEONTYFVVwIbHirS/vHfD1F8aY2NAXR3Q2grU1Igvni8/Px/FxcXIz88f5/vyl4Hf/S4PALBooSsntjeTvuXL81E8pQT5F/8SODCm1l/i4kvaFz63uOVj6ytdCFj3ArZOwOcF8gvGv8zM2AoCwPcIUGdn0EVvJv7S4N4BWDXfH/4QuPtr+x/G2OXLdV9MxCe+XPVtXxS4ENr+RnJeQQiDbQE0rhN0ApOMUwdglXxjY2PU09NDIyNj43z33BMogHp7c2N7M+nTx3ZsbCxhnyHEx9o35hoJ3f/M8rH26f+OPBHxqePmliAYRJ0CiMjQJOPWAVgln3476YoVPeN81dXazzjttNzZ3kz64rUYYL8IiS8pX8/2Fcnfqq3Q9qbM9/JXNd9L10b1yW3wglnUKoCIYk4y7h2AuftGRsZoxYoeKiwcG+ebN0/7Oddemzvbm0lf1L9SVViExJe0b+zRQurZvoLGXCMs86nskyNAglnUK4CIIk4Krh2Ac8HX3x84/fWTn/DLl4u+pBCf+MQnCHFRswAiCpkUnDsAq+SbPn2AvvvdJ+n48YGQp7a1BQqgbduyl09l38DAAD355JM0MDAw/pthPpaLhviS0g3YjtOT//ddGvjNdJb5lPC9+JWIvrhzSxCiwPY2+Lgo0gFYJV/hlx7ARRc5UVDgC3lqe3vg60WLEBkFtzeTPp/PB6fTCZ/PN/6bQT4A6tyiLD7D+FAA57SL4CvrBY5sZJePtW/oPWDn54Dhd4FZl4zzxZ1bghAFdQsgKNIBWDXf5ePfpPQC6NRTgTlzspxPUZ/FYsHy5ctjCzkvQuJLymexWLD8hjrAVwvslj5QCTHlFGD0qPb17E8CU88I8RmaW4IQAWULoECTuSKgtkVbfD4w/zED4gPo/ScwuKsQJZ95CAVFxf7n6gVQ1KM/im5vJn1erxcjIyMoKSlBQcH4Zm5+uC5C4kvKF7L/GeZj7SuYBFjO1z4Ow9YJfG5HiM/7iQ0YcY7Fn1uCEE62z8GFY+QaoIgXnCZxzll8ms9/q+72FX6f1Rq4/ufHP86t7c2kL+FbdVW4JkN8hhm3/5nlC6e8vJxXvr/Ua6/dMo3I5w3xpaTFgMK0t7dTY2MjVVRUaPtNMIxyBRD3jr0q+1yjQ3T4918n16NFft+OHYEC6A9/yK3tzaTP5XLR4cOHyeVyxf0ZyeQTH09fxP3PKF8wTU1NBICa1j3AJ9/bTYGGiAMHQ3yuR4sSn1s5gl78WCwWAiAFUIIoVQCp2AFYdV/jf3v8BdCxY/zyqewzhPjEl2FfeXl5YDHlkq//tUAB9G7LeB/RhP4w1MbGRimATKBMAaRqB2CVfA6Hg7Zt20YOu9Xvq/3cnwgg+vCHs59PZZ9/bB2OGC80n098vH0x9z+DfDptbW0EwP+vra2NRz6Pk2hTkfaa9u+FfMtht9K2bdtowYIFE7YI0o/aSQGUGHwLoP2d/sdU7gCsks9qtdL69evJarX6ffPnvk0A0Ze/5M16PpV91uO9gbE1A4dFSHymfSFzi2E+ncrKypBTKpWVlXzybavQnt92RcjDVquVPv/5zxMAKYCkAEoIvgXQhioiL/+Ovbnss/W7/ae/7vrmZnb5VPQlhfjEl0ZfV1eXv4jQT6kAoK6uLhb56M83a8994kNEPp//YX3xlwJICqBE4VsANeZGB2CVfD6fj9xuN/lOvLns3Bm4APqZf/tC1vOp7PM9lkfuF2vJ50nyQs1sL0LiM+ULn1vc8hERNTQ0UH19PRFp19PoRYX+WCxfV1cXVVZWUmVlJVVXV1N5eTmVl5drp9CCiPi8U6ZS2/fH5+vq6vI/x2KxUOWVl1HbUw8SeT3+57S2tvqPVukFQHl5OVVXVxseonjZW1tbqaKiImKR1dDQ4L9mKvymapvNRk1NTVRRUUHt7e3+7bFYLGSxWKi6unpcwWbmNUSRC6DwsamoqKDW1taQ1+mFrsViGfc9M9hsNqqvr6eKigqqr6+n6urqkN8rbrAtgPb/poqoBbTlVm0RMrX46Hjd5H2pRnxxCL9Vd82aQAH0wZM3Zz2fyr7ubeNbDCTjy4WiYCL5EmqDkKXttVgs1N7e7v//6urqiAt7uK/r7YOB64VOoB9NMvzY/3wmJF9TUxNVVlb6jz61t7f7C43GxsaQOH/5y19MHwEymrO9vT3qzwi+biqY4KNo+mnF6upqqqys9D9usVgCR9hMvoYo+hEgfVsAUFNTU8QxKC8vj/q9RNFz6mNks9nIYrGM22dcYFsAXX1VJ225NfCXeDJvAm430bJat/ji4HQ66eDBg+R0OomIaOlSrfg5+WQe+VT23bBskA4++Q1yPjpJ2UVcfOZ94XOLWz79qEMwwYv+uAUsyNdwwzkRC4PGxsaQowoNDQ3Rn7dlk9/X9j+fifi81tbW8afliOiNN94wXQDFzBSUPbiQCH9u8PfC0R/3X0t1gvb29vHXWSXxmlinwPSiJPw1wdlTRaRxaGhoSMnRpXTAtgACDtCy2sBf4mbfBIKv4RBfYr6zzgoUQBzzqeZTfREXX+76ysvLIy5S+qmfiNeWnPDVf05b9Br+/baQb3d1dYUcUaqvr9ee19AQ+XknfBVngiovmjNue4NPyzX867dDXm+2AIqbKcLPNlMABbt0Il5nZfI1sQqg4CNU4UeOGhsbEzpdGA/95wT/Lumn8jjCtgCqqjpgqMNuLCJewCq+qK8fGhqiXbt20dDQENntgdNfXPKp7Fu27MTYOmzKLpLiM+8Lnlvc8rW1tZHFYon49OCjLhH/ive6/UdsAFDl4sVRF7vghTj49FYwtv7ewPU8p0z1X4/j/3daKZXPzaP6yiKtIzQRvf7666YLICOZiNJTAAU7g8fWzGviXQStF7Lh1+JEuk4rGfSC0mKxKHFBOtsCqLMzqA+QiTcBsx17J7Kvv7+f1q5dS/39/fT884Hi51Of4pFPZd+xY4GxVXURF595X/Dc4pav+qvX+xfVWP/CT5EF+xq/fX7Ic+vr6yMugMFHMCI9Ty9Iqq88NfL2/n2d9ngLiAb+TkSxr88xQrxMROkpgIjIf0or+BocM6+JVwAFF7J6/q6urqiFr1n0a36inXLjBtsCaFwn6ATeBLh3AFbBd8EFgQLovff45ZtIPkOIT3wmfLbmE4vt3r+QzWaL+E//qz7WoqwfCSqfEygkwi+q1mlrawu5cyr4eXoBVFGxMPL2Hn81UAC9u5GIkjsFZiQTUfoKIP1nBh+FMfMaI7fBh19E3tDQMO7UXyqIe9SQEeoUQESG3gS4dQBW2QcQfehDfPNNBF9CiE98CfoabjiHKj8e2xe8wMe8XuREvqZ/AllOKvIXEtFoamryHy3QnxdSzPT3jt9ezyjRxkKyNYOo/bbxr0nytEukTETpK4AiOc28xkgBpD9H367y8vKUXZsTPib6hddRjxoyQa0CiCjmmwDHDsAq+Y4e7aU77ribysp66aSTNO811/DJp7Kvt7eX7r77burt7TXsM4X4WPp6Hyqju398B/UeO8oqn8ViodYffzKuL1ofHCKK2Ceo638CR4L0RTZSL5jg4kF/XvCpqIjbu20BVV8CorYriYjo1VdfNV0AGc0UXACFFw3Bp+DCiVXM6D8nvEgw8xqjjRD14k7v1ZMKurq6xh1JijUmnGCXLm4BRBRxUnDvAKyCb+nSUbrwwn301a+O+o8A3XEHn3wq+0ZHR2nfvn00OjpqyJcU4mPnG31hKe1bdyGNvrCUTT792hcjvuBuy+GLXX19feipjhO+yo8j4I/0vBPoRwt09FvT/T8rKF/7ryup/MPTtMaJj08n8nmpu7s7YtFgpBgymokoUJiE98wJPkUY/jP1xyNdaKxvZ3ihY+Y1Rgug4OudYvX+aWhooOrqakNHiNra2iIWU0byZBs1CyCikEnBuQOwir62tsApsN/+ll++XPdxXcTFlzs+/WJV/2meOL7wD0kNXujr6+vHN+fzuslyUhE1fDHgi/g80o5KBBdVNpst5IiTvpDqp9VafzBv3IXQ+vUtFRUV/iaKRgsgI5n05wafCqyvr/ffRRX8eHBxEpw/+HG9YIlUhJh5jV4YGbmoOd7RsuBi10gBoz8/uJDUxyRVDRbThboFEBGRl38HYJV8Nyy30yuv7KGf/nTYXwB98AGffCr7hoeHac+ePTQ8PBzTx2mRFF/qfMPDw7Rn9ys0/MKyrOdrbW0NueC3srJSW7yi+MI/7kFfaPXFTT9aoHctrq6u1gqRdQ+E+Br+/bbIz4uySDY0NIT0Iar+6vXUteXqQPHTAqJ3N9Hw8DD95je/oXnz5vndRq9tiZo9Rqbgj6XQf055eTk1NDREPZrT2tpKlZWVgY/1qKyMe42Pkde0t7eHHDHTi7BYFx/r2xmNrq6ukP0dr5BsbGz0f9yF/t9IH7vBEaULIBU6AKvk69leR/fddx/deGMfAURz54Z85mDW86ns6+vro/vuu4/6+vpiSxkv4uIz7/Pv/55ulvmU8r18faAA+usdxudWFoh3QXOqXpMIifT+SfVt8txQtgBSqQOwar6zz/YRQPSFL/DMl4u+EFRYhMQnvmz62j6bOl8a4VYA6Z+pZoS2tja2H2KaKpQsgFTqAKyab+DBaf7TX//5n/zy5aIvIuITn/iM+5jCrQCqrq429MGk7e3tSjQyTJakC6DGxkaqrKykyspKqqioiNoB1CjxCiDVOgCr5Ovr6aZ77voRzZqlnQJ7+skxVvlU9pk6TK/aIiS+qETc/4zyqezre3gW+1NgiXzchJnXRKOxsdF/p5t+/ZLR100EkiqAKioqqLy8PKTg0S8oM1u9xiqAuHfsVd03PDxM996zm6ZM0S6Cfv+JlazyqewzdBF0JBRZhMQXm6j7n0k+ZX3Ofhpuu87c3EozwbecV1dXGzowYOY10QjuZ5TKoiqXMF0AVVdXRz1MF3J7ZYJEK4C4d+zNFd8NN2jPmTPTRr7H+OXLFV9CiE984gtzeIieOY+oJY+o/XvJ5UkD+t1k4f9i3RZu5jXx0I/6pPpDT3MFUwWQ3uUx2sVUer8EMzvOXwDt7/Q/lsmOvRPZNzo6SlddtY+Ki0fp6s972eVT2Tc67IjeCNEIHBch8Rn2xWyEySCfkr4/XEDUAhp9dklyc0uYsJgqgPSjP9GuENc/DM1MF0h/AbShisirfgdglXzvvttLt9+ufRTGD3/IL5/Kvt7tKyJ/FEYicFyExGfIF/WjUJjkU9K35xva3Hq0PPm5JaScsbExuueee+jll1/OdpSomCqA9M8TiXaEJ9JnqRjFXwA18u/Ym2u+l14KdIB+6il++VT3sVg0xCe+XPG9db/2uhYQOd5JLoeQcl566SV/HbB48WKWhVAhEuTQoUOw2+0AgPLy8ojPKS0t9X/d0dER9Xmx8M2tQv4HrbhuLoDaFmx4rAhFRQlrgPwi4LIW+HwQXxzfq68Gvl60iF8+5X1HWrXHLmvRHjfDCR8AQHzim8i+0kWBr/tfBabNN5dBSAter9f/9c6dO7Fz504sXrwYP/rRj/DpT386i8kCmCqAdIILnWAsFkvE54fT29uLvr6+kMfeeecdAMBnb7sDPucmFBc5YZl9GB/5yIfhdk/B5MkjKC4ewcDAbADAjBnH4XSWwOUqwaRJTpSUDMJuLwMATJ9uhds9GU7nVAwPb0CZ5b8w4igBCgbx8Y8DXm8RRkamobDQjWnT7BgYmAWfrwAnnWQHUT6Gh6ejoMCD6dOtcDhK4fUWYupUB/LyfHj//S2YWrQWc8t6MDhsxRlnzEFJyRAKCjwYHJwJgDBzZh+GhmZgbGwyiouHMWmSCw5H6Ykx6sPIyDS43cWYPHkMbvdajA78H4qLnJgx6wjOPvtDcLmmxtymoiIXTjppADZbGYA8TJtmg9dbiJGRaRgaehRzZv4XnENT4MsbxHnn5YGoIOY2DQ35cMstm7BvXxXmzDkDwCQMDg7C6fTg/61twbVzgc8ufBFfOuNrKCp4FMPDbrhcLv/vQV9fH6ZNm4bi4mKMjIxgZGQEs2dr++n48eMoKSlBSUkJBoe9+M6Dv8A1M4HaS1vxudJJcDvvR1GRBS6XCwMDAygrK0NeXh5sNhsKCwsxbdo0uN1u2O12zJo1CwUFBbDb7cjPz8eUKdPx9QcewdUnT8bySzaj+vQ6DA2vwzAKYLFY4PV60d/fD4vFgkmTtG3yeDyYOXMmiAh9fX2YMWMGJk+ejOHhYQwNufDdtS247sT2fvF0bXtHRsaibpPT6cTg4CDKyrT9ZLVaMXnyZEydOhVDIz78y2/uQsXH5mO5uxl5ozeh8JL7MW3GzKjbNH36dHg8HlitVpSWlqKwsBAOhwM+n0/bpk9sQP/QZFje24JJqMPg+U3w+BB1m+LupxOL0PF3XkDJ2I0ouWIDnG5v1G2KuZ88BPtZv8AsH1BwpBX20SLkX/xLTLfMir1NsfbTJx9D39BkzHjvcUxGHYYvbIZrzJfw755/P53YXus7OzHZfSOmXrkBrjFfwr9706dPh8eXB+tZv0CpNw+FRx6Hw1kIX8UvYSmd7d8mIsITTzyBL33pSzjppJPi76fLWtA3NBnT3t2KYtRhZMGDGHGOJfy7599Pn3wMeQBsXW0odH0d0z77iLafzPzuUT76z/oFLN48TDryOAZdhfAs+F/MnFVm7ndv9mzgshYcH56EknefRAnq4KxYj8FhZ+xtKl2AvMKT8O7AbDyzcQ/qyu/CSRf9CPbpiwPb9MfvIf8fv8f0ScPw+ApgdU9H6SQHCvO9cLinwoc8WM76ArwLfhb43et+CoN/uR0eKsDMSYMgAvpcMzGjaAiTC8Yw7CmGq+h0lF77YmCb8gdQ/PJnMOKZjBFPMWYXD2j7yTkDJYVOlBS64PROwuDFv0PZGRcEtulPX8ZUzyG4vEUYGDsJZZNtyMsDbO5pKMzzYlrRCNzn/4zvNpX/K4ov/Peoa3w43Aqh/ERfoB/9AUILnWj09/dH/d4DDzyAj3/84yH/rr32WgDAkiXbYRsuRbf9FFy/dAsslr+juxuYO3c/rrnmEXR3A93dwDXXPIK5c/ejuxuwWN7C9dc3+7+3ePFmnHHGXnR3Ax/60GF87Zsb0Tc0B939pbj88qfxkY+8gu5uIC/vGGpr18HhGEF3N3DRRdtx/vk70d0NuFxW1Naug8tlRXc3cP75O3HRRdvhdObBjWLU3vgE8ia70N2dh4985BVcfvlT6O4G+vo8qK1dh+Li99DdDZxxxl4sXrzZn+3665thsbzl36blyzf4t/eaa5/G3Lmvx92m4uL3UFu7Dn19HnR3A5df/pR/m2bO7MHXvrkRjrEZ6O4vxUUXPRt3mxyOyXjnnbNQVbURPT3HAACvvPIKfv/7p7BpSxGu/1kL1h35Zxz58F1AfhH27t2LzZs3+/dnc3Mz3nrrLQDA/v378cgjj/i/98gjj2D//v0AgEOH3sLZH30Ytb98HLiB8Pihy7C3fR8A4L333sO6devg8XgAAE899RReeeUVAMCxY8ewbt06jIyMAAC2b9+OnTt3oqgI+MUvB3HIchasVUeR/5nHsXPXy9i+fTsAYGRkBOvWrcOxY4FteuqppwAAHo8H69atw3vvvQcA2Lt3L554YjM2bSlC7S8fR/M//gVvz/4+kF8Uc5veeustNDc3+7+3efNm7N27FwBw9Oh7OP2Mx/HRikpMrjmCp95fjFd2/znmNgHaG+S6detgtVoBaG8e/m1yjmHdX87Csc+8A1z+OF7Z/eeY2xR3P+UXAZc/jkf6v4/9U/4FyC+KuU1x91PTgxhZuB64gbD9+Bexc9fL8bcp1n7y5WHdX87CexfvBy5/HHvb95n63fNv04nt3TzyE+zNXwnkF5n63fNvU9ODsH7sV8ANhJ2DNdj+XFvINtntdpx77rnYt2+fsf2UX4Tm9o/irQv+DFz+OPYfeNPU755/m3x5wOWP4ynvz/CK+2tAfpH5372REaxrehDH5v0UuIHwivtreOq3vzf/u3diex85sBD7P/ICcPnjeOvtQ/G3iQqAU6/Dzv5KTMkfxWT3Bzh2fCB0m/YXYefR84HRblgdLqz7ey2sDhcw2o2dR8/H9vcvAtz20N897yhe6f4InnrvcmC0G56RPqz7ey3esxYDo93Ye+wMbH5rQeg2vXMYGO3G/t65eKTrGmC0GxjtxiNd12B/71xgtBtvHbeg+bHfhW7T0dnAaDfesxZj3d9r4RnpA0a78dR7l+OV7o8Ao928t+n9IZhh586d+MxnPoPKykr88Y9/NOVIBXlERIm8YOvWraipqQEAtLe3o6KiIuLzZs6cCbvdjvr6ejQ1NUV8TrQjQNdeey1qa/+EGTMuAwAUFvbB650GomLk548gP38EHs/sE987Dp+vBD5fCfLynCgoGITHo/3VUFBgBdFk+HxTkZfnQkHBwInv5aGgwAaiQvh805CX50ZBgR0ezywABSgo0I4A+XzTAXhQWGiFx1MKoBD5+drREq/XAsCLwsJ+eL0WEE1Cfv4g8vI88Hq1I0Ba7hkgmoz8/GHk5bng9Zay3qY5c7z45jf7ceqpxo6WmDkCZOgv1kT/Cjd7ZEG2SbZJtkndbXL2wtb+cxR6BzCt2Af3aV+DvfCcwDb99UHk29sxvdgHjxewjhSitMSDwgLA4cyHj/JgOe0SeM+8MbBNA69h8I0N8PjyMLPEqx0tGSrEjCleTC4kDLvy4covRenl9wS2qciN4rfuwog7HyPufMw+SSuijw8VomSSDyWTfHCO5WHwzNtQdurZgW16+6eYin64PHkYGC1A2Uke7QjQSAEK84nFNg058/DyX/swKc8FABgYLcDkIh+KCwnuGRdh0imRj+Ls27cP//7vxo4OLViwAKtXr8ayZcsMPT9VJFwA7dixA0uWLAEAtLW1obKyMrI4Lw8A0NDQgMbGRsP+jo4OLFq0CE8//TTOOuusRKIJgiAIgpBC/vjHP2LVqlUZ+VkjIyOYMmVKRn4WYOIaoIsuusj/dfDpsGjMn5/YhWmvv/46APhPhQmCIAiCwJfbbrsNH/nIR0Iee+utt/A///M/hl4/e/Zs3HzzzRktfgATBZDFYoHFYoHdbvefHw4n+MLnaEeIonH22drhwccffxznnntuovGEJNBPP8rRt9QjYzuxkf2fPmRss4c+9jfccMO4y2F27doVtQDKy8sDEaG8vBz/+Z//iRUrVqCwMOFyJGlM/cTa2lo0Nzejq6sr4vf1AshisSR8C/z06dMBAOeeey7OO+88M/GEJDnrrLNk7NOEjO3ERvZ/+pCxzR6TJ0829Dy98Jk3b15WCx+dhO8CA4CVK1cC0K4HikRbm3YHxPe//32TsQRBEARByAX0a4LnzZuHhx9+GG+99RZuuummrBY/gMkCqKKiApWVlejo6IjY56e5uRkWiwX19fVJBxQEQRAEQV24FT46pgogAGhtbUVFRQVqampCLobWjw7t3LnTUJ8gQRAEQRByi0svvRTf/OY3WRY+OqbTWCwWtLe3Y82aNaipqfFfGF1eXo53333XdPFTVlaGH/3oR/7+D0LmkLFPHzK2ExvZ/+lDxjZ7xBr7KVOmYP369VlIZZyE+wAJgiAIgiCojulTYIIgCIIgCKoiBZAgCIIgCBMOKYAEQRAEQZhwSAEkCIIgCMKEg00BtGbNGixZsgRLlizBokWLsHLlSkOfNSYIgiAIE5mtW7di0aJF2Y6hHCxuyl+0aBHsdjva29v9t8/X1NRg3rx52Llz57jPGBEEQRCEiU5HRwduvvlmdHR0SN89E2T9CFBNTQ06OjrQ2toasgNbW1sBAIsXL85SMkEQBEHgyZo1a7Bly5a0nSmJ9lFXuURW+wB1dHRg0aJFKC8vj/jBqitXrkRzczOamprkYzViYLfbsXr1arz22mu46KKLYLVaUV5eDrvdjqampmzHUxYZVwGQ34N0ImObPM3NzVi5ciUsFgtsNltKnB0dHaipqYn6gee5QlZPgd17770AgMrKyojfX7JkCZqbm9HY2CgFUAxqamqwY8cO2Gw2f0fuefPmyYfRJomMqwDI70E6kbFNntLS0mxHUJasFkD6IbZoF2/p1/4cOnQIhw4dQnl5ecayqYQ+jvopRP2DaIPHq6OjAzt27EBXVxdWrlwp11UZQMZVAIz9Htjtdrz22mvo6OhAZWWl/B4YRMZWyCZZuwbo0KFD/nOX0Qqb4Mq2o6MjE7GUZuvWrf6vly5dGvJGUV5ejr1796K5uTltbyCHDh1CTU0NVq5ciZUrV2L16tVp+TmZJtvjqtPc3IwlS5ak9WcI0Yn1e2C1WtHU1JS23/lc3/fZGNtcfb/iQEdHh//U3KJFi9Dc3Oz/3urVqzFz5kz/zU86W7duxfz58zFz5syMXX+U1QJIJ9ohvOCLooOfL4Sinx68+eab/b9QFRUVIYWlfng5ncXPokWLsGTJEjQ1NaGpqQl2u13pN20O4wpofyWvXr0aK1eulHmQBYz8HpSXl6O8vBwWiyWlvwu5vu+zNba5+H7FDbvdjubmZv+ROwD+8S0tLfUfPQe0omjLli2oqKiA3W5HY2NjZkJSlmhtbSUABIC6urqiPk9/TkNDQwbTqYXNZiOLxUIAqLKyMurz0jmO1dXVZLFYxuUCQE1NTWn5memGw7gGU15eTuXl5Wn/OUIoRn8PysvLqbq6Oi0ZcnXfZ2tsc+n9Sl9Lw7cnGdrb25P+fWtvbycAfk99fb1/ra+srCQA1NbWRk1NTf4x17cl1u+CTltbGzU2NoZ4EyXrt8EDiHkbn34USJoiRsdiseDBBx8EoP3FGHw4WUc/hbh06dKU/3y73Y6tW7eitrZ2XK7y8vLMVfMpJtvjKvDAyO+B3W7HoUOH5AhCgmRjbHP1/Yob+tGdyspKrFmzBqtXr/Yf2Xvttdf8z7Pb7f4jgW1tbQBgaF9XVlb6Lz8we31w1gqg4NNbVqs16vP0wkeaPI0nuCisrq72H2bU764LRv9l1A8x1tTUYObMmSFvOGvWrEFNTQ3mz58PAP43nXjnZPXv6a8LpqKiIuR6LxXgMq5CdjHzexDtjlb9du/Vq1f7u97n4ikto2RzbHPt/YorejGjF5Z6kdLR0eG/bKC1tRUNDQ3+18Tb1+EkffmBqeNGKUA/3AiAWltboz5Pf45qhyXTTVdX17jTLvohx0i7tbKykiorK8lms1F9fT3ZbDYqLy8fd6jRYrH4n9fQ0ODfT/X19VGzNDY2Rt1H9fX1BIDa29tNbmlm4TSu4eTqaRCOJPp7UF9fH/UUhH46oa2tzf9YZWVlQvsyl/Z9tsc2l96viMydAgu+BMXsv3g/T39e+HucPv4WiyVknPX3xES2A0lefpDVI0D6UZ1oR4CCq3ijFeFE4dChQ+OOHuiVcKTDgTt27MCSJUtw7733oqmpCRaLJWJrAf1CQL3/kk6kv5Z0+vv7AcTuRxHrKB8nOI2rkD3M/B5Eeo+y2+1YvHhxyFEO3TVRjzRke2xz6f3KLNXV1SCiqP/a29tRXl4e8zmxmi4G79/wu+v0I0PhLQ0SPfqjPz+ZU6NZvQZIPwcbrdukXgDph9CEAIcOHUJHR0fIqZbgK+qD0R/v6uryNxjTr12pqakZ97z+/n7/OVn9XG11dXXULPoiHutNQ5VmXZzGVcgeifwe6NeoRLoOrKamBna7fVxjv4l8+ivbY5tL71dcCS5ywtdufV+HX2tl5PqflStXoqamBjU1Nf6Py0rm4EhWGyHqH3UR7ToIfUCkK+h47HY7Ghoa0NbWhr179/qbhbW2to5bVPVxrKmp8R91i1Rt679QF198sf95bW1tcQvQiy66yJ8pHP1NRpUCltO4Ctkjkd+DaH+52u127NixA/X19eOuYezo6PDf2j3RyPbY5tL7FVf0/bZy5cqIj4e3Ogj+nn5gxG63h9wEtWjRIjQ2NqK6uhp2ux0zZ85M/syQ6ZNnKUK/HS7SbWwWi4UsFgvZbLbMB8shKioqxp0Tr6iooIqKipDHIl1nYOT201jXs0T62blCusc1nFy6DiSXqK6ujrhf9Osswq81aWtri3vtYzgTdd+nY2xz7f2K223wwdf3htPQ0EAAqLGxMeJr9J/Z1NTkv65Lb5UQvL+6uroiehIl67fBt7a2oqKiwn84U0evHHfu3Dkh/0pKJR0dHeMq8Y6ODv9hZbvd7j/UHPy84NtP9edEwmKxoLq6etyRPLvdHvFn5wrpHldBDYKvUQnuWK8fTdCPOOjof8XK6c/4pGNsc+39au/evQD4tIrRxzXSPoh2RC/4tvg1a9aEPEc/LRp8ykw/zZnsHMp6AWSxWNDe3o6lS5f6z+3p5wDfffdd+dyXJIn0C6e/kVRUVGDNmjWwWCx4/PHHAYT+Qum/lBdddBGam5v9heihQ4eQl5cXcr6+sbERVqs15Lz+vffei/Ly8pDbHHOFTI1rOLl+caZq6Bfb6he4Bx/W1383gt/c9Y8E0E+LBntk34eSzrHNhferrVu3+m/911myZAnWrFmT1WvMDh06hIqKiqi90SJ9nttFF13kfx8sLy/3Xyt56NAhNDc34/vf/37IgZDW1tbUXEKQ1PEjgT2NjY3jTsnot5zqt2UTaYcmw2/dbm9vJ4vFMu5UjX5IMtxrs9mourqaGhoaqL6+3n9beC6SyXFtb2/3HzrGicO+Kt2mm+vo+yzSafzW1laqqKigxsbGkI634ci+j0w6x3YivV8lSio6QacC/Xc/eP+baSESDSmABNMke/5ViIyM68RF9n36kLE1TldXl6GPo0g31dXV464l0ns16dd5JVO0Zv0UmCAIgiAIfCgvL/ff5ZpN9HYE+qnKNWvW+NsYWCyWkEsIzJDV2+AFddFvNRVSi4zrxEX2ffqQsVWTxsZGvPbaa6ipqUFlZSWampr8t8e3traiqakpKb8UQIIpduzYoczFgioh4zpxkX2fPmRs1US/SSqcWF2oEyGPiCglJkEQBEEQBEWQa4AEQRAEQZhwSAEkCIIgCMKEQwogQRAEQRAmHFIACYIgCIIw4ZACSBAEQRCECYcUQIIgCIIgTDikABIEQRAEYcIhBZAgCIIgCBMOKYAEQRAEQZhwSAEkCIIgCMKEQwogQRAEQRAmHP8frfV+zwLT4i8AAAAASUVORK5CYII=",
      "text/plain": [
       "Figure(PyObject <Figure size 600x600 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject Text(0.7, 0.03, '\\\\shortstack[l]{Asset supply, $s$}')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Figure 7(a): low y_d (under low ρ)\n",
    "\n",
    "## range of asset supply\n",
    "mD = 0.1 # dealer size\n",
    "xRange = range(1e-3,1.0+mD-1e-3,length=1000)\n",
    "\n",
    "## initialize output\n",
    "di = Dict()\n",
    "\n",
    "## compute\n",
    "guess=[0.99,0.01,] # set some initial guess\n",
    "for x in xRange\n",
    "    diPara = Dict(\n",
    "        :s  => x,:mD => 0.1,\n",
    "        :λu => 0.1,:λd => 0.1,:r=>0.05,\n",
    "        :values => (1.0, 0.9, 0.0), # yh, yd, yl\n",
    "        :searchB => (1.0, 1, 0.5), # ρB, nB, qB\n",
    "        :searchS => (1.0, 5, 0.0), # ρS, nS, qS\n",
    "    )\n",
    "    converging,θLO,θHN,diB,diS,diX = solveEquilibrium(diPara,guess=guess)\n",
    "    diX[:plannerθLO], diX[:plannerθHN], diX[:plannerWelfare] = plannerTechChoice(diPara)\n",
    "    di[x] = Dict(\n",
    "        :converging=>converging,\n",
    "        :θLO=>round(θLO,digits=5),:θHN=>round(θHN,digits=5),\n",
    "        :diB=>diB,:diS=>diS,:diX=>diX\n",
    "    )\n",
    "    guess = converging ? [θLO,θHN,] : rand(2) # recursively update guess based on the previous eqm\n",
    "end\n",
    "\n",
    "## find thresholds for s\n",
    "s1N  = findlast([di[x][:θHN] for x in xRange] .== 0.0);   s1 = xRange[s1N]\n",
    "s2N  = findfirst([di[x][:θHN] for x in xRange] .== 1.0);  s2 = xRange[s2N]\n",
    "s2pN = findlast([di[x][:θLO] for x in xRange] .== 1.0);  s2p = xRange[s2pN] # p for planner\n",
    "s1pN = findfirst([di[x][:θLO] for x in xRange] .== 0.0); s1p = xRange[s1pN] # p for planner\n",
    "\n",
    "## set up canvas\n",
    "fig = PyPlot.figure(figsize=(5,5), facecolor=\"w\", dpi=120) # create figure\n",
    "fig.subplots_adjust(left=.1, right=.92, bottom=0.12, top=.9) # reduce white spaces\n",
    "ax = fig.add_subplot(111) # create axis for total trading cost\n",
    "\n",
    "## plot thetas\n",
    "ax.plot(xRange, [di[x][:converging] ? di[x][:θHN] : NaN for x in xRange], ls=\"-\", color=\"blue\")\n",
    "ax.plot(xRange, [di[x][:converging] ? di[x][:θLO] : NaN for x in xRange], ls=\"--\", color=\"orange\")\n",
    "\n",
    "## vertical lines\n",
    "ax.vlines([s1,s2,s2p,s1p],ymin=0.0,ymax=1.0,linestyle=\":\",lw=0.8,colors=\"gray\")\n",
    "\n",
    "## fill\n",
    "ax.fill_between([0.0,s2],[1.0,1.0], facecolor=\"none\", hatch=\"//\", edgecolor=\"blue\", linewidth=0.0)\n",
    "ax.fill_between([0.0,s1p],[1.0,1.0], facecolor=\"none\", hatch=\"\\\\\\\\\", edgecolor=\"orange\", linewidth=0.0)\n",
    "\n",
    "## set axes ticks and ranges\n",
    "xmin, xmax = 0.0, 1.0 + mD\n",
    "ax.set_xlim([xmin, xmax])\n",
    "ax.set_ylim([-0.005,1.05])\n",
    "ax.set_yticks([0,1])\n",
    "ymin, ymax = ax.get_ylim()\n",
    "ax.set_xticks([0.0,s1,s2,s2p,s1p,1.0+mD])\n",
    "ax.set_xticklabels([\"0\",L\"$s_{hn,0}$\",L\"$s_{hn,1}$\",L\"$s_{lo,1}$\",L\"$s_{lo,0}$\",L\"$1+m_d$\"])\n",
    "\n",
    "## reference lines\n",
    "ax.hlines([0.0,],xmin=minimum(xRange),xmax=1.0 + mD,linestyle=\":\",lw=0.8,colors=\"gray\")\n",
    "\n",
    "## format axes\n",
    "ax.spines[\"right\"].set_visible(false)\n",
    "ax.spines[\"top\"].set_visible(false)\n",
    "arrowed_spines(ax) # add arrows\n",
    "\n",
    "## label axis next to arrows\n",
    "ax.annotate(L\"\\shortstack[l]{$\\theta_{hn}$ (solid line) and $\\theta_{lo}$ (dashed line)}\", xy=(0.03,1.0,), xycoords=\"axes fraction\") # left y label\n",
    "ax.annotate(L\"\\shortstack[l]{Asset supply, $s$}\", xy=(0.7,0.03), xycoords=\"axes fraction\") # x-axis label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HUzDa29v51C4RXC4XdHd3Z32JhiksPGeKH56LjJFwAcQwDMMwTMlB7h6g6dOnmx0CwzAMwzBFDrkC6Ne//rXZITAMwzAMU+SQK4DOOeccs0NgGIZhGKbIIVcAMQzDMAzDGA0XQAzDMAzDlBxcADEMwzAMU3JwAcQwDMMwTMnBBRDDMAzDMCVH3gXQ9u3bYfHixXrEwjAMwzAMUxBy/i6wvr4+uPLKK6Gvry/rLwFlGIZhGIYxk5wKoA0bNoDdbgeXy6VzOAzDMAzDMMaTUwHU3NwMAACLFi2CNWvW6BoQwzAMwzCM0eR1D9CCBQv0ioNhGIZhGKZg8FNgDMMwDMOUHFwAMQzDMAxTcuT8FJjhHGoFmDw+9fuWcwBO/Zb6f9e/Ad5+IL1z6hyAD61V/x8aB3jtN5lj+eDVANOPjYrtTwABe/p1Tv4GwIKo9gDvPgzg6E2/Dm+TRJFtUyQC8M/eOfD4O+o2TSsbh/MXZN6m55xXgy+ibtN5lj/BMVPSb9MhzzdgJKBu05mzHoaTZqTfptHAOfCaR92m46f9G86ek34/BSJzYL+TtwlA2zY5QwvhRfd3AKAso49hROHGG82OIHdMLYCsVivYbLaY1954442j/9gE4E+z8mmrEpPQyzel/4EzT0xMrJnWAQA4fXVsYv3PnwDGX0+/zjEnxSbW4YcBBu5Ivw5vk0SRbVM5AJzuPxFuukndphMt4/C/rZm3qenm1fD6YXWbDm38E5x5fPpt2rrzJLijR92mtu8/DF/4Uvpt2vrsKripVd2mVef/G676Ufr43nOeCF/lbQIA7dv0VJcT/vDoTzL6GEYURC6ATL0Edvvtt8NHPvKRmD+XXHKJmSGVNN7wLHjS/gXwhmclfT88CVBfD1BWJv0diRQ4wAyEwrHxIZodkYovMgO+8IUnYdYsr9mhMCYgz60rl7WbHUrRMWuWl+cWkxuYB52dnQgAaLFYclp/dHQUX3755Zg/Dz74IAIAvvzyy/mExuSA3W7H1tZWtNvtCe8Fg4h1dYgA0t/BYH4/q1R8r74qORYssOPata24YIGdVHzsK4zP/uyN2HrzVWj/6wLEgDO9bDKIk0/VIW4BvO/qOlxZH8wvviL3JRy3iMVX8j7CmFoAJePll1/mAogYoicfM31bt0oe+Q+1+NhXIN+7DyNuAenP4Scz+lbWB/G+q6UkNPlUHeJk7kGyj31m+ijDBRCTlqJIPib61q5Vi5/ly+nFx74C+bzvIG6bifjoJxFHujX5Vtarv4njM7klIfaxz0wfdbgAYhSsViuuX78erVYrIhZR8jHRd8IJkm/hwtixpRIf+wrjs46OZtz/SX2TQSn55JCESsUnH7eGh60k4ytVnwhwAcQo+Hw+PHjwIPp8vqJKPmb5amvVsz/f/rY6tlTiY1/hfNFzK2tfDkmolHw+nw97ew/iihU+kvGVok8UuABiEjA7WRSTT/7z29/SjI99gviySELsY5+ZPpHIqwBqbm5GAMA8HyaLgQsg8/B6vbhv335cudIrdrIg4rvgArUAeuwxL+7fvx+9Xi+Z+NhXOJ/X68X9zz6D3sMvIb51n3RPULY+DUmoFH0rV3rxk5+UjlsU4ysln2jkVLl0dnbGFD8AgDU1NdjS0oIDAwN5BcQFkHnYbDa8/vqNeOyxtqJKPmb5brpJLYDeeMOGGzduRJvNRiY+9hXOZ7PZcOPN/4u2vx4rJZCBv+bmS5OEStV37LHScWtkRMPcKoLtpeoTEf1O3egEF0DmQiFZFItPvgfo1FNpxse+Avu2AOLWcsQtgOHnfpy7L0kSyis+9rEvH5/AcAHExEAmWRSBb9Ei6bWLL6YZH/sK7JOTxhbAV//w2fx8UUlo8impWR372GeGT2TIRc8FkHnYbNlfpomHbPIpsG9sTL389YtfaB9bUbeXfemR979128cQtwAG7q7AVfX+/OKbJN4BuEC+nI9bgm4vOZ/AcAHEKHi9ud2oK0M1+Zjhe+YZtQDauVPb2Iq8vexLj/yAQVvzH5SzQJN7L8zrHopgkHYH4EL5cj1uibq91HwiwwUQowuUk48Zvj/9SS2A3nqLXnzsM8f3qTOeVb8Sg1DHXvaxL1efyJALnwsg88jUrC0VoiSfQvq+9z3pfYsFMRJJP7bFsL3sS+9bscKH5557EL+z2oaRLWVS8nn4QyQ69oru8z2xAg/29Wo+bom+vdR8IsMFEKMQ/1UYWhAh+Zjhq66WlvnCF6T/pxrbYtle9qX3VVZa8YYbpK9rwIc/KCWenotM79hbDD7rXypx/S9vQOvhYZLxlYxPQLgAYnJGlORTaF8wiDhtmrTcNdfQi499JvueWYG482TE/d8rqg7A7GOfaHABxOSEsMmnAL4XX1Tv/7n7bnrxsc9kXzgQu3ARdABmH/tEhAsgRsFut2Nrayva7fa0ywmdfArgu/tutQB68UXpteixNTs+9hXel3FuCdwB2Gyf3W7H1ttuQ/tjDSTjK0mfIHABxCh4PB7s6elBj8eTchkRk0+hfddcIy0/bZq6vDy2TqfH9PjYV3iflrklXAdgIj5lbN1OkvGVrE8AuABiNCNq8im074tflNaprqYZH/sI+ILjiNZ9iO89Hvt6VBKi3gGYfewTHS6AGAW/34/9/f3o9/sT3iuq5GOgLxKRHn0HkB6Flxkf9+Pll/fjtGn+otpe9mnzJcytJy6UftPe9eFE2aQgHYCJ+BLGllh8Je8jDBdAjMLo6CjeeOONODo6GvO66MmnkL633lLv//njH1VfY6M0to2No0W1vezT5kuYW/+67uiXo05BDE0k+EToAEzFFz+21OIrdR9luABiFCKRCAaDQYxEIsprxZB8Cul78EG1AHrmGdVXVhbB+vogBgKRzBID42OfOb6EufXWfWo36CPPJ/VR7wBMxRc9thTjK2UfdbgAYlJSLMmnkL5f/EItgI4coRcf+4j4xg6pBdDr7al9hDsAs499osMFEKPgcDiwo6MDHQ4HrWQhkO/iiyXHokWxPqtVHVsz42OfOb7ouYWIiJFJxPtmSYnl+avS+6h3ADbZ53A48M47O3D1agfJ+ErRJwpcADEKbrcbd+/ejQ6Hu6iSTyF9p50med7//lifPLZut9vU+Nhnji/p/n/sfOkei0c/ndlHvQOwiT6Hw41XX70bZ892k4yv1HwiwQUQEwOFZCGqz25XL39RjI99Jvvik8bzV0lf5nn3LCwvC2f26d2xl33s09knGlwAMQqBQAAvu2wIp04NmJ8sBPQ9/rha/FxwQawvEAjg0NAQBgKB1AKD42Ofeb5AIIBDD1+GAZ/aCDHUf4dyH9CPv3tIm0/vjr1F4Js6VTpueTwa5lYRbC9Vn4hwAcQoyI+T8qPaufnOPVctgN58M/b9VC0GChkf+8zzKft/T6PSYXfdlQcwcPdUHGz9GIYOP6ddliQJ5X1mSmCf3GJC69wSfXvJ+QSGCyBGIRwO46pVbvT5wnl5qCWfQvoAECsrpYaI0YTDYXS73RgOZx5bkbaXfdoIh8Po7l6F4c3lSofd8rIwrqwP5BZfVBIi2QG4gD6fT/vcMiO+YveJDLnouQAyFwrJQlTfvHnS38uW0YyPfSb7Jol37GUf+3LxCQwXQIyC0+nErVu3otPpzGl90snHYN/y5YgVFdK/r702cVktYyvS9rIvO5xOJ27evBUbG2xkO/aK6sv1uCXq9lLziQwXQIzC2NgY7tixA8fGxrJel3LyKYTvuefUS2BbtyYun2lsRdte9mXHkSNj+JOf7MC5c8diO+w+XYs49h/Et3ciBpw5xUexA3AhfWPOI1kft0TeXmo+kSEXPhdA4kE9+RTCd+edagH06qv04mMfMV/0PRTyn+E9+viKrKMw+2j7RIYLIEYhFArh6OgohkIhzesImXwM8P3Xf0mvzZyJmOxezFRjK+r2si87X0VFCBsbR3FiImr/TwYRn/ymWgD9+1f5xZdDUisGX+ieChzd04ihwERKTzFtL1mfgHABxCjwo9q5+y64QHr9vPOSr5tsbEXeXvZl50v5qPZkEHHbTCkJ7Ty5oB17i8U3uqcxpsUAtfhKyicYXAAxCtmcARIp+Rjtm5xEnD1bem/NmuTrx4+tyNvLvux9ExNp5lbP19SzQIJ2ADbTFwpMSGeA7qkgGV/J+QSCCyAma0RLPkb7Xn9dvf9n0yZ68bGPuO/Fn8feCyRYB2D2sU9UuABiFLQ8BWZ6siDo6+xUC6D9+5N75LE9cmRM+O1lX/a+tHPr7R1q8dP9RXE6ABPxKWPrPEIyvpL1CQAXQIxCpn4aoiYfo33XXy8tU16O6PUmX0buA7N6tVP47WVf9r60c2t8UC2A+v8oTAdgKr6YsSUYX0n7iMMFEKMJkZOP0b6LLpKWO+ssmvGxj7gvEkG8f55UAP3zSnE6ALOPfYLDBRCjkOr7qkglC4K+k06Sll25Mp0vjHPmuLGuLiz89rIve1/G74Lr+jzi9krEA1djMChGB2AqvvixpRZfqfsowwUQo8CPamfvGx1V7//59a9T+yorpbEdHtb4jdU6xcc+Gr6MLSaC44iRSIyPegdgKr7osaUYXyn7qMMFEKMQCARwaGgIA4EAIhZP8jHS99hjagH02GOpffX1ARwYUMe2UPGxj4Yvfm5p9hHuAEzFJ4+txxMgGV+p+kSACyAmKcWUfIz0tbSoBdDhw/TiY18R+Kh3AGYf+wSFCyBGwe124+7du9HhcIubLArsW7VKWu/EE9P75LF1u90FjY99NHzp9r/smzF1An965fMYGknSS4F6B2ATfQ6HG6++ejfOnu0mGV+p+USCCyBGweFw4J13duDq1Y6iSj5G+j70IWndiy5K73M4HNjR0YEOh6Og8bGPhk/a/3cm7P9o30Br9dFeQEuTS6h3ADbJt3q1A7/3Pem4RTG+UvKJBhdATAwUkoUoPq9X6v0DgPi//0svPvYR82XosDv5j+9KyaVzgfRofDKodwBmX8n6RIQLIEYhEolgRUUQ6+oi5icLAXz//Kd6/8/Wrel9kUgEg8EgRlIlNgPiYx8dXyQSweA9FRh5ui51h93Xfq82RPS8nVqWJAnlFZ/wvgh6vdrnlvjbS8wnMFwAMQry46T8qLY2/vxntQC68ML0voyPQRsQH/vo+JT9/5fK1B12R59SC6B3HkovjEpCJDsAF9A3PKx9bpkRX7H7RIZc9FwAmYff78dXXulHv9+fs4Ni8jHKt2aN5KmoyOzz+/3Y3595bClvL/ty9/n9fux/7RWc2LsidYfdgEstgF66KbN0kngH4AL5tM4ts+Irep/AcAHE6AbV5GOU77zz1DNAFONjHz1fxg67Dy6UCqCnLtXHp3d87GNfnE9kuABiFDweD/b09KDH48l6XRGSj54+nw9xyhTJt2hRZl+msaW+vezLz+d0evCqq3pw1ixP+g67T10qvf7gQs3xUewAXEifx+3M+rgl8vZS84kMufC5ADIPu92Ora2taLfbs1qPevIxwveVr6hnf9raMq+TbmxF2F725edraLDjVVe1YkODXfKleprmpV+ql8ECTu3x5fF0jug++2MN2HrbbZqPW6JvLzWfyHABxOSFCMnHKJ/854UXaMbHPuK+ZEnonYcQt5Yj7vowouu1/H16x8c+9uk4P8yGCyAmZ4ROPnn6PvhB6e8pU6TLYdTiY58gvvgkFBxHDE3o50uT1NjHPt19gsEFEKNgtVpx/fr1aLVaMy5bFMknD9+yZdK/zzlH2/rxYyva9rIvP9/wcJq5ZULH3mLyWQ8P4/pf3oDWv1SSjK/kfALBBRCj4PP58ODBg+jLcEpDtOSjty8QQKyslP7/7W9rc0SPrWjby778fRnnlsAdgM32+Xw+PNjXi74nVpCMryR9gsAFEJMVIiYfvX3Dw+r9P7/5Db342CeoLzoJPfl1xHcewqA/oI9PhI7C7CsunwBwAcQoeL1e3L9/P3q93qTvk0oWJvoeeUQtgPbu1ebyer24b99+XLnSK9z2si9/X6a5pTAZRHz0U8qTYOuueD6/+KKSGvWOwrn6YsaWYHwl7SMOF0CMgs1mw40bN6LNZkt4T+Tko7dv/Xq1ANLaMWBkxIbXX78Rjz3WJtz2si9/X7q5lYDjX0oB9Nwvl9DtAEzElzC2xOIreR9huABiMiJ68tHbV1srvXfqqTTjY5/gPn8IA3+boRRBVDsAs499osMFEJMW8snCBN8ZZ0jvf/ObNONjn/i+5375CcQtgJHO48h2AGYf+0SHCyBGIf5UsijJopC+sTH18tcvfqHd19CQxSWQPOJjH02flktg0b6u/7tSSjz3z0V8upZkB2AqPnlsR0ZsJOMrVZ8IcAHEKETfTFhMyUdP3zPPqAXQzp3afS6Xxptg84yPfTR92T5gEH61Vf1KjLF+2h2ATfZlfMCgyLZXBJ8ocAHEJGB2sqDs+9Of1ALozTfpxce+IvFZn1ULoLcfoN8BmH3sExAugBgFn8+Hvb0HccUKn1jJooC+739fWtZiQYxEtPu0NpnMNz720fQpzfri9n9KX3AccUuZlHAO/kx6jXoHYJN8K1b48NxzpeMWxfhKyScaXAAxClarFW+4YT1WVlqLKvno6auulpb/whey82XzNSP5xMc+mj6r1Sp9XcPhYe2+h85EvN+C+K/r1NeodwA2wVdZKR23hoc1zK0i2F6qPhHhAoiJgUKyoOoLBhGnTZPW+clP6MXHPuK+bDvs+mzJTzNS7wDMvtLyCQwXQEwMZJIFQd9LL6n3/9x1F7342EfcR7ljL/vYl6NPZMhFzwWQedjtdrzttla0a21vnASyyUcH3913qwXQiy9m57Pb7djamnlsKW0v+/Tz2e12bL3tNrTtaaDbsVdQn9a5ZVZ8Re8TGC6AGAWPx4M9PT3o8XhyWp9q8tHLd8010rrTpknfCJ+NT8vYUtte9unn83g82N3dgw2rnNl12A0HEB0vIjoTj4fBIO0OwIXy5XrcEnV7qflEhgsgRhcoJx+9fF/8orT+xz9OMz72ieHLqsPuzlOl5f7RoI9P7/jYV/I+kSEXPhdA5uH3+7G/vx/9fn9W64mUfHL1RSLSo+8AiN/9bva+dGNLcXvZp69vxQo/nnlmP65Y4Zd8Wp+meeKr0jK7zk4fXx5P54ju8/eswP7XXtF83BJ9e6n5RIYLIEZhdHQUb7zxRhwdHdW8jgjJRw/fW2+p9/987GPZ+1KNLdXtZZ++vspKaf8PD0ftfy1J6F8/ld7fWo4YmkgfXw5JrRh8o3+plObWyDDJ+ErGJyBcADEKkUgEg8EgRpI9epsEUZKPHr4HH1QLoFx8ycaW8vayT29fBL3eJHMrUxJ66z6lI3To8POZ48siqWnaXgF8kafrMHhPBUaephlfSfkEgwsgJifESj75+37+c7X4ueQSevGxT2BfuiQ0dkgpgP78/9ry9+kdH/vYJzBcADEKDocDOzo60OFwpF2uqJKPRt9JJ0m+WbNy80WPrQjbyz59fRnnVqokFJnEyLZZiFsAW7/7Q+3xpUlqOW0vYZ80tnei4/HVJOMrSZ8gcAHEKLjdbty9eze63e6Uy4iYfPTyASBeemluHnlsHQ63MNvLPv18WuZWqo69r/3xfMQtgP1//HR28RndAZiITxlbl4NkfCXrEwAugBjNiJp89PDJf371K5rxsa9IfFFJSO7Ye9t3r0LcAtKZoMlw3j6942Mf+0SFCyBGIRAI4NDQEAYCgYT3SCaLAvk+/3m1ANq1KzefxxPAyy4bwqlTA+S3l336+9LNrQQmYzv2tl+7Sfqt/O9nIE68l31wk4J0FM7RlzC2xOIreR9huABiFPhR7eS+W29VC6B3383N19gojW1j4yj57WWf/r5sWkwEg3Ede3suRfTn9/U0InQUztUXP7bU4it1H2W4AGIUwuEwut1uDIfV0+zFkHzy9X3nO9L/jzsu+Zdza/GVl4dx1So3+nxZXsLQEB/76PuSza1MPuodgKn4oseWYnyl7KMOF0BMSool+eTr++hHpdeWLaMZH/uK2Ee4AzD72Cc6XAAxCk6nE7du3YpOp1PMZGGAz+9HrKiQXr/22tx9Nps6tnrGlyvsK6wvem5l7YtOQl2fp9cB2GSf0+nEzZu34urVTpLxlaJPFLgAYhTGxsZwx44deOTIWFEln3x8vb3q/T9btuTuk8d2bGxM1/hygX2F96Xb/5p8/X9UGiLik9+g1wHYRN+RI2P4k5/swLlzx0jGV2o+keACiImBQrKg5OvoUAugV16hFx/7BPLl02F3pFstgKh2AGZfyftEgwsgRiEUCmFj4yhWVITMTxZEfP/1X9L7M2ciZrh/Na0vFArh6OgohkIhXePLBvaZ5wuFQji6pxFDgYncfP4javGz6yN0OwCb4KuokI5bExMa5lYRbC9Vn4hwAcQoyI+T8qPaKp/5jLTMeefl58vmMehs4tMK+8z1Kft/T2PuHXZ3niIlnqdraXcALrBPbjGhdW6Jvr3kfALDBRCjIJ8B0vSbVBqoJZ9cfZOTiLNnS8utWZOfL5szQMUyfuxTUc4A3VORe4fdJ78hJZ2HzoxJQiQ7ABfQNzGR/dlVkbeXmk9kyEXPBZC5UEgWVHyHDqn3//z5z/TiY59gvsk8O+y+eMPRy2BliMHx/H16x8e+0vQJDBdAjEI+TyohEk8+Ofi2bVMLoOeey8+nZWzN3l72GecbGxvD7dt3YOPqI7l32H17h3ofkHUfBoO0OwAXypfrcUvU7aXmExkugBiFTL1K0kE5+eTqW7dOWn7KFMSJidj3svXl1QcmB9hHy2ezOXHt2q04b54z9w67428qBVD41T8p8VHsAFxIn9Nuy/q4JfL2UvOJDLnwuQASD+rJJ1ffl78srfPhD9OMj30C+3J5miYSQeycj5Ftx+DW69fn79M7PvaVpE9kuABiFLR+X1E0QiYfDUQiiJWV8lMm+ftSjS2V7WWfsb6k3wWXS8de19tYXxdOHl8uvnTbK4gvvLkc3d2rMBz0kYyvZHwCwgUQo8CPaqsMD6v3//zmN/n7ko0tpe1ln7G+lI9qm9ixt1h8o3saY1oMUIuvpHyCwQUQoxAIBHBoaAgDgUDGZUVKPrn4du1SC6AnnsjfFz+21LaXfcb6PJ40c8uEjr3F5Av4PDj08GUYuGcqyfhKzicQXAAxWSNa8snF96tfqQXQ4cP04mNfkfmy6dgbCCO6XkUcH9DHp3d87GOfIHABxCi43W7cvXs3ut3ulMuQSBYF8C1fLjlOO00fnzy2Doeb5Payz1iflrmlqWNvYBJx+3HSMgeuTh+MFl8220vUp4yty0EyvpL1CQAXQIyCw+HAjo4OdDgcSd8XNfnkwsKFkuekk/TxORwOvPPODly92kFye9lnrC/T3FKISkIpO/Y+ep6UpB6/IHNQWnzZQNAXM7YE4ytpH3G4AGI0IXLyyRanU738RTE+9hW5bzJDx97n1kgF0H2zESOT+fv0jo997BMELoAYhUgkgsFgECORSMzrpJOFAb6uLrX4+cxn9IovghUVQayri5DbXvYZ70s1t1L50nbs/c+f1Y7QY//J35cl1HzxY0stvlL3UYYLIEaBH9WW1j/3XLUAGhzUJ77KSmlsh4c1fmN1Bh/l8WNfIlpbTET7UnbstT2nFkBv3Ze/L8ftpeKLHluK8ZWyjzpcADEKfr8f+/v70e/3I2LxJJ9cfACIxx0nNUTUw7dihR9feUUd23x91MePfbHEzy3NvmRP54QmELdOkV7710/z9+WzvQR88tiOj/tJxleqPhHgAohJSjEln2x9c+dKfy9bRjM+9pWYL1kS2vVh6f9PfEUfn97xsa+kfaLABRCj4PF4sKenB51Oj7jJIk/f8uXSl58CIDY36xefPLYej0cXXz6wr/C+dPtfky8+Ce1bLf37geMTTlPm5Mu3A7CJPqfTg1dd1YOzZnlIxldqPpHgAohRsNvteNttrdjQYC+q5JON79ln1Utg996rX3x2ux1bW1vRbrfr4ssV9pnjs9vt2HrbbQn7PytfdBJ65Fzp752nIgac+fsodhTW6GtosONVV0nHLYrxlZJPNLgAYmKgkCzM9LW1qQVQfz+9+NgnsE/vjr1PfpNeB2D2laxPRLgAYmIgkyxM8v3gB9Jrs2YhTmposVLo+NgnsI9qx172sS8fn8BwAcQoWK1WvOGG9Tg8bM3LQzL5aPR98pPS6+efr298VqsV169fj1Zr5rEVefzYlxyr1Yrrf3kDWv9SSbNjr8C+4WHtc8uM+IrdJzLkoucCyDx8Ph/29h5En8+Xs4Ni8tHqC4cRZ86U3vvRj/SNz+fz4cGDmcdW5PFjX2p8Ph8e7OtFb/cKfTv29lxCtwNwgXxa55ZZ8RW9T2C4AGJ0g2ry0ep75RX1/p8776QXH/vE9+nZYTf8yu/VhogEOwCzrzR8IsMFEKPg9Xpx//796PV6s15XhOSTybd5s1oA9fbqG1+msS2G8WNfalwuL65Zsx9nzvTq1rH311c/pBY/j32eVAfgQvq8466sj1siby81n8iQC58LIPOw2Wy4ceNGtNlsWa1HPflo9a1dKy1TUYGYrmFzLvGlG9tiGT/2pfY1NNhw7dqN2NBgk3w6dOx9/4J31DNAr95KpgNwoX22RxuyOm6Jvr3UfCLDBRCTFyIkH62+L31JWu7cc2nGx74i8+XdsTeCkc7jpPX/8W0dfHrHx76S8gkIF0BMzgidfOKIRBDnz5eW/e536cXHviL15duxd+8yad1dH9HHp3d87Cstn2BwAcQoZHMJrCiSTxRvvaXe//OHP+gfX/zYmr297Cusb2QkzdzKp2NvX7O03tYp0pek5utLBnGfbXQEN958Pdr+eizJ+ErOJxBcADEKWm+CFi35aPHt3KkWQE8/rX980WNLYXvZV1hfxrmVa8feN+9V7wM68nz+vlQQ9nm9Xtz/7D70PrGSZHwl6RMELoCYrBAx+WjhhhvUAmhsjF587CsBXy4de8f61QLo9bb8fXrHx77S9QkAF0CMQqaGYqSShc6+b3xDWu+MM4yJT24yuWKFj8T2sq+wPs3N+qKSkKaOvZFJxPtmId47DfHl/8vflwmCvpixJRhfSfuIwwUQo5Du6xpETj5aOPlkdV0j4hselr5mpLLSSmJ72VdYXzZfhYKTWXbsdb+R/gmebH16x2ewL2FsicVX8j7CcAHEZET05JMJm029/HXzzfTiY1/p+UToAMw+9okOF0BMWkRIFvn6urrUAuihh+jFx77S9FHvAMw+9okOF0CMgt1ux9bWVrTb7YgoVrLIx7dhg1oAyfcC6R1fQ4Mdb7tNHdt8fZTGj32ZffFzS7NPa5+WSARxfADR87Y+Pr3jM9Anj+3hw3aS8ZWqTwS4AGIUPB4P9vT0oMfjKarkk4lVqyTPjBnGxed0qmOrh4/S+LEvsy96bmXty5SEJkOI2492hO77f/n79I7PYJ/H48Hu7h5cudJDMr5S9IkCF0BMAmYni0L7PvhB9QwQxfjYx76MSeiRj0rvdS/Vx6d3fOwrGZ9IcAHEKPj9fnzllX5cscIvdrLIAqdTLX7OOsu4+Px+P/b396M/3besZuHTOz72Gevz+/3Y/9orCfs/K1+6JPTsZYhbACOdC7CuLpK/T+/4DPStWOHHM8+UjlsU4ysln2hwAcQojI6O4o033oiVlaNFlXzS+eQvQAVAvPde4+KTx3Z0dFQXn97xsc9Yn7L/R4bz86VKQq/9XmmIeMqxQ/n79I7PQF9lpTS2w8Ma5lYRbC9Vn4hwAcQoRCIRrKgIYl1dpKiSTyaf/OeNN4yLLxKJYDAYxEgkootP7/jYZ6wvEolg8J4KjDwtJY28fEmSUGj4KaUAarn6wbx9esdnrC+CXq/2uSX+9hLzCQwXQEwMFJJFIX0LF0p/z52LODlJLz72FZHPwI69l68+ohRA4X/9Im8fuY7C7CPrExly0XMBZB4OhwPvvLMDHQ5Hzg6yySeFr7pa+vfnPmdsfA6HAzs6Mo+taOPHPm1I+/9OPPLoasM69o60LZIS0pPf1MVHrqNwCp/WuWVWfEXvExgugBgFt9uNu3fvRrfbndP6VJNPKp/XizhtmvT///5vY+PTMraijR/7tON2u3HXrt3YsNphWMfeyI6TpQJo5ym6+Kh1FE7ly/W4Jer2UvOJDBdAjC5QTj6pfC++qN7/c9dd9OJjX3H6DO3YuwUQH3gfYnCcZnzsKzqfyJALnwsg8wgEAjg0NISBQCCr9URKPtG+u+5SC6AXXzQ2vnRjK+r4sU+7r74+gKeeOoT19QHJZ0TH3ie/SaIDcKF9gSfrcejNAc3HLdG3l5pPZLgAYhRK7VHt//5v6fXp07X/nFzjSzW2Io8f+7T7kj6qTaBjbzH4Rv9SmdBigFJ8JeMTEC6AGIVwOIxutxvD4bCm5UVJPql8n/uc9N7ixcbHl2xsRR8/9mXjC6PdnmRuFVEHYLN84afq0H33HAxnuKelWLaXtE8wuABickKs5JPom5yUHn0HQLziCnrxsa+EfHp37A04EffWCN1RmH0C+wSCCyBGwel04tatW9HpdKZdrhiSzxtvqPf/tLYaH1/02BbD+LEvO1/GuaVXx97Xfou4tRxxSxniU5cK2VE4W5/T6cStWzaj8/HVJOMrSZ8gcAHEKIyNjeGOHTtwbGws5TIiJp9kdHaqBdCzzxofnzy2R46MFcX4sS87n5a5pUvH3qHt6tNgo0+L1VE4R58yts4jJOMrWZ8AcAHEaEbU5JOM//1fabmyMkSPh1587CtRX74de8cH1ALoUGv+Pr3jY19p+YjDBRCjEAqFcHR0FEOhUMJ7JJNFHr4LL5SWPeuswsQ3MRHCxsZRrKgIFcX4sS87X7q5lUA+HXsjEcT750kF0D+vyN+nd3wG+BLGllh8Je8jDBdAjEIpPar9vvdJy69aVZj4GhulsW1sHC2K8WNfdr5sWkwEg3l27O36vFQA7Vmsj0/v+HT2xY8ttfhK3UcZLoAYhWS/pRZD8olnZES9/2fDhsLEV1EhnQGamNBwBkCDr5j2Ryn4tJ4Bivbl3LH3hZ9I69w7DYP+YP4+vePT2Rc9thTjK2UfdbgAYlJSLMknnt271QKoq4tefOxjn+LLpWPvwN3KfUD/8/2X8vfpHR/7SsInAlwAMQrRT6oImSw08n//pxZAR44UJr4jRzQ8BZSFr5j2Ryn4Mj0FltaXbRJyvqQUQN++4O78fXrHp7NvbGwMt2/fgatWjZGMrxR9osAFEKMg9yqx2ZxFlXziqa2V1j3llMLFp7XHklaf3vGxz1hfuv2vyZdNx15/EAN/m46hv03Bzf97S/4+vePT2WezOXHt2q04b56TZHyl5hMJLoCYGCgkC6N9ixZJ63/zmzTjY1+R+grYsfdD738FV6/w6ebTOz72FadPNLgAYhTC4TCuWuXG8vKw+cnCIJ/LpV7++tnPChdftt+zlsmnd3zsM9YXDofR3b0Kw0Fffj69O/YWga+8XDpu+Xwa5lYRbC9Vn4hwAcQoyI+TFvOj2k89pRZA559fuPiyeQxai0/v+NhnrE/Z/3sa8++wmyQJlbJPbjGhdW6Jvr3kfALDBRCjEAgE8LLLhtDjCeTloZZ8ovnDH9QCqJDxBQIBHBoawkAg89hSHj/25eYLBAI49PBlGLhnauE69kYiiJ63ESfe08end3w6+Twe7XPLjPiK3Scy5KLnAshcKCQLI33f+Y5a/NTW0ouPfUXsmyxgx95wAPGB46UEdfB6evGxr3h8AsMFEKPgdrtx9+7d6Ha7c1qfdPI56ps3T/Idf3xh49MytiKMH/tyw+12465du7FhtaNwHXsfPksqgHou0send3w6+XI9bom6vdR8IsMFEKPgcDiwo6MDHQ5H1utSTj6yb/ly9ezPNdcUNr5MYyvC+LEvd6xWB157bQdaLI7Cdezdt0p6bceJ+vj0jk8nn+OINevjlsjbS80nMuTC5wJIPKgnn2if/GfzZprxsa9EfHk8TaPZ98oGpSEiThymFx/7isInMlwAMQqRSASDwSBGIhHN64iUfJYsUQugV14pbHypxlak8WNf7r6ysgjW1wcxEIja/zkkobTxxfuG96gF0PCe/H16x6eTL7K5DINP1mMknOcDBoJsL1mfgHABxCgU+6PaP/yh9O+ZMxGzaMejS3zJxla08WNf7r6Uj2pnkYQ0xRfte/KbagH08s35+4h2KB7d0xjTYoBafCXlEwwugBgFv9+P/f396Pf7My4rUvKRfXLfn09+svDxxY+tiOPHvtx94+Np5paRHXu3zUzaryVnH8EOxf6JcezfcTn675lGMr6S8wkEF0BM1oiWfIJB6YzPrFnSaz/4Ab342FfiPiM79m4BxL+fQTc+9hWfTxC4AGIUPB4P9vT0oMfjSbkMiWSRg6+/X73/p62t8PHJY+t0eoQcP/bl59Mytwzp2Pvw2ZLv3mmIQXf+PoIdipWxdTtJxleyPgHgAohRsNvt2Nraina7Pen7oiYfRMR771ULoOefL3x8drsdb7utFRsa7EKOH/vy82WaWwpRSUiXjr2etxF7viZER+FcfTFjSzC+kvYRhwsgRhMiJx9ExOZm6b0pUxB9vuQOM+NjH/sUROkAzD72CQ4XQExGSCcLjb5ly6T3P/IRmvGxj33RPhE6ALOPfaLDBRCjYLVacf369Wi1WpXXREgWmXyRCOJxx0nLfPvb5sRXWWnFG25Yj8PD1swrmRAf+4z1JZtbmXx6duydVuHHtd8/SLqjcK6+6LGlGF8p+6jDBRCj4PP58ODBg+g7eo2oWJLPO++o9//89rfmxLdihQ97e9Wxzdcn8v4oRV/83NLs06Fj7/9ctBFD91RgZGsFYnCcbEfhXH3y2LrdPpLxlapPBLgAYpJSTMnnoYfUAqinh1587GNfWl+eHXv/cM1m9XF4ey/9jsLsE94nClwAMQperxf379+PLpdX3GSRhJtuUgsgp9Oc+OSx9Xq9uvj0jo99xvrS7X9Nvnw69h55RS2A3rgzf5/e8eXpc7m8uGbNfpw500syvlLziQQXQIyCzWbDjRs3YkODraiSzyWXSMsvXGhefPLY2mw2XXx6x8c+Y32p9n9Wvlw79k6G1Y7Qz/8of5/e8eXpa2iw4dq10nGLYnyl5BMNLoCYGCgkC719p50mrbN8Oc342FciPjM79j76SWmdx87Xx6d3fOwT3iciXAAxMZBJFjr57Hb18tevfkUvPvaVkM/Mjr3P/UBa/r5Z0hmhfH16x8c+cX0CwwUQo2Cz2fD66zfiyEh2l2nioZR89u5VC6Bdu8yLL5tLYJTGj336+Gw2G268+Xq0/fVYczr2vt6m3gc01p+/T+/48vCNjORweVng7aXmExly0XMBZB5erxf37cvtRl0Zasln40a1ABoeNi8+rTdBUxs/9unj83q9uP/ZfTjevdKcjr1HnlcLoDfvzd+nd3x5+HJ+wEDQ7SXnExgugBjdoJh8Ghqk9Y8/HjEQoBcf+0rLZ1rH3rAPcesUxC1liK+00IuPfcL6RIYLIEZBS7O2VFBNPmefLTm+/GVz48s0tlTHj336+NxuH15xxUGcMcNnXsdeex9icFw/n97x5ejzed1ZH7dE3l5qPpEhFz4XQOahtV1/PFSTj9eLWF4uec46y9z40o0t1fFjn36+xkYrXn/9emxslL6uIZ+naZLGV8I+657GrI5bom8vNZ/IcAHE5AXl5PPPf6r3/1CMj30l7sshCbGPfWR9AsIFEJMz1JPPbbepxc9Xv0ovPvaxL5skpJtv4jCiz0o3PvaJ6xMMLoAYBbvdjq2trWi32zMuK0LyqaqSfBUViH6/ufHFj60I48c+/XyHD6eZWxqSUFbxpfKFJhB3nIS4BTB88Kb8fXrHl6PPbjuMrbf+FO1/XUAyvpLzCQQXQIyCx+PBnp4e9Hg8aZcTKfkAIJ5/vvnxRY+tSOPHPn18GedWoTr2PrgQcQvgP2+9VB+f3vHl4PN4PNjzRDd69q4kGV9J+gSBCyAmK0RKPvIN0P/1XzTjYx/7YihAB+DJJ5cjbgEc/N3pJONjXxH5BIALIEbB7/djf38/+lNcLyKVLDL4vvxl9QxQR4f58fn9fnzllX5cscIvxPixT19fprmlEJWEjOgAfN/PblQaIgY9jrx9FDoUx4wtwfhK2kccLoAYhdHRUbzxxhtxdHQ04T3Rks9f/6oWQH195sc3PCyNbWXlqBDjxz59fenmVgKTxnUAfvrnF6gdoUf25u2j0KE4YWyJxVfyPsJwAcQoRCIRDAaDGIlEYl4XMflcc430/6lTpQ7Q5scXwYqKINbVRYQYP/bp60s1t1L5jOwArPx5daMuPrM7FMePLbX4St1HGS6AmLSImny+8AXptepqmvGxj32ZfIZ2AN4CiPtW0o2PfUXhow4XQIyCw+HAjo4OdDikewNEShbRvslJxLlzpde//30a8a1e7cA771THNl+fSPuDfYlzS7PPiA7AD7xP8t03Rx+fyR2K5bG1Wh0k4ytVnwhwAcQouN1u3L17N7rdbqGTzxtvqPf/tLbSiM/hUMdWD59I+4N9sXMra5/eHXv/tU49C/T0cnodhbP0ud1u3LVrN65a5SYZXyn6RIELICYBs5NFvr7OTrUA2r+fXnzsY1/WPj079nrfQXS9ivh0Ld2OwuwT1icSXAAxCoFAAAcGhrC+PiB0srjuOun98nLpC1EpxBcIBHBoaAgDWd6RXRTJm33S/n9zIGH/Z+Wj3gHYJF99fQBPPVU6blGMr5R8osEFEKMgP04q+qPaX/mKtMzZZ9OJL6vHoE2Ij33G+pT9PzKcn496B2ATfJWV0tgOD2trMSD69lL1iQgXQIxCOBzGOXPcWFcXFjb5RCKIlZXScg0NdOILh8PodrsxHA7r4tM7PvYZ6wuHw+i+ew6Gjz5SnJdPr469kyFE9+v6+fSOT7MvjHa79rkl/vYS8wkMF0BMDBSSRT6+d99V7//5zW/oxce+EvZR6tj72u8Qt81AvHcqYpg7KLMvd5/IkIueCyDzcDqduHnzVnQ6nTk7zE4+Dz2kFkA9PXTiczqduHVr5rE1e/zYZ4zP6XTi1i2b0f7Yahodewc3q0+C2Xvz9+kdXxY+rXPLrPiK3icwXAAxCmNjY7hjxw4cGxvLaX0Kyeemm9QCKP54aGZ8WsaWwvixzxjf2NgYbt++AxtXH6HRsdf1qloAvXFn/j6948vCl+txS9TtpeYTGS6AGF2gknwuvlhap6qKZnzsYx+Jjr2TYcRtx0jrPH8VvfjYJ4xPZMiFzwWQeYRCIRwdHcVQKJTVepSSz6mnSuvV1tKKL93YUoiPfcb66utDWFk5ivX1IclHoWPvY5+WzgTs+ZQ+Pr3j0+gLPVmPoyPDmo9bhY6v2H0iwwUQoyD6o9o2m3r56//+j1Z8qcaWSnzsM9aX9FFtszv2Pv8jxC2A/rtnYnlZOH+f3vFp9I3+pTKhxQCl+ErGJyBcADEK2Z4BopZ8Hn9cLYD27KEVX7KxpRQf+4z11deHcHg4ydwysWNv6NCdyn1AP/neK3n79I5Pqy/0ZD2O/qUSQ0/Wk4yvpHyCwQUQkxMUk09Li1oAvfMOvfjYx76kmNCxNxhEvPaKPqUACr2+mVx87BPUJxBcADEKWp+moJp8VqyQHCedRC++6LGlOn7sM86XcW4VsGOv7Js6JYChe6ZKy7z0y7x9esen1Tc2NoY7HtiOY12rSMZXkj5B4AKIUdDST4Ny8vnAByTPiSfSi08eW5vNSXb82GecT1OvGhM6AIcOP48YSBOTyfFp8Slja7eRjK9kfQLABRCjGcrJZ2xMvfxFMT72sU8TUUmIegdg9rFPdLgAYhTSfV8VyWQRxRNPqMXP+efTi8/nC+OqVW4sL0/ytA2B+NhnrC+r74KbFKQDMBFfwtgSi6/kfYThAohREPlR7Y99TC2A3niDXnyNjdLYNjaOkhw/9hnry6bFRDBoQgfggAPRZ9XPp3d8aYgfW2rxlbqPMlwAMQqBQACHhoYwEAgor4mUfAAQjz1W+kZ4avFNnRrAyy4bQo8nkHklE+Jjn7G+ZHMrk68gHYAnQ4h/P0N67eD/5u/TOz4NRI8txfhK2UcdLoCYlIiUfObNk/6uqaEZH/vYl5OvEB2Ad50t/f+Jr9KMj31C+kSACyBGwe124+7du9HtdguVLJYvR5wyRfp3czO9+OrqEB0OdWwpxsc+Y33Rcytrn9EdgPcdfXz8geNTnj41Nb4MPrfbjbt27cZVq9wk4ytFnyhwAcQoOBwO7OjoQKvVIVTyefZZ9RLYvffSiy8YVMfW4XCQjI99xvrS7X9NPiM7AD9yrtIQEb3D9OLL4LNaHXjttR1osThIxldqPpHgAoiJgUKyyNb35z+rBdChQ/TiYx/7EJFex95on/zn3Ydpxsc+IXyiwQUQoxCJRLC+PohlZRHzk0UWvjVrpNdmz0acnKQXH6I0tsFgECNZ3KFNKnmzLy9fJBLB4JP1GAnn+YCBER2An7pYLYBe/AW9+DL4ysqk41YgoGFumRBfqfhEhAsgRkF+nFS0R7U/8Qnp9QsuoBkfYnaPQZsRH/uM9Sn7f08jvY69k0HE+2ZJvh0n0YwvjU9uMaF1bhU6vqL3CQwXQIyC3+/Hyy/vx/Fxf16eQiafYBBx+nTpvR//mF58Mn6/H/v7+9Hvzzy21JI3+/L3+f1+7N9xOfrvmUazY+/Ty5WzQCTjS+MbH9c+t8yIr9h9IkMuei6AzIVCssjG99JL6v0/d91FLz72sU9hknDH3pf/D3ELYGTrFJrxsY+uT2C4AGIUPB4P9vT0oMfjyWl9M5LPXXepBdCLL9KLT0bL2JJO3uzLy+fxeLC7uwcbVjlpduz1vovBI6/g6hUTNONL48v1uFWo+IrdJzJcADEKdrsdW1tb0W63Z72uWcnnxz+Wlpk+Pf3PNDs5Zhpbs+Njn7G+w4ft+NOftuKCBXaSHXtF9tlth7M+bom8vdR8IkMufC6AxMPM5PPZz0rLfeITNONjH/uS+gh17GUf+/LxiQwXQExemJl8JielR98BEJua6MXHPval9RHo2JvgC0wijg/SjY99dH0CwgUQo2C1WnH9+vVotVo1LW928vnPf9T7f/78Z3rxRZNsbCnFxz5jfY2NKeaWiR17432h/jsQ75strRt0k4svlc+6pxHX33Q9Wo+2GKAWX0n5BIMLIEbB5/PhwYMH0efzZVyWQvLZtk0tgJ5/nl580cSPLbX42Gesz+1OM7dM6Nib1Pf2TrUh4ugz9OJLgc/rxoP3X4G+v80gGV/J+QSCCyAma6gkn+ZmaZ0pUxCj8wqV+NjHPs2+AnbsTenzDKkFUP8f6MXHPnF8gsAFEKPg9Xpx//796PV6Uy5DIlkcZdkyab1zzqEZXzTy2LpcXpLxsc9Yn5a5ZXoH4EgEcfux0vLPXkYvvhQoYzvuIhlfyfoEgAsgRsFms+HGjRvRZrMlfZ9S8olEEI89Vlr3O9+hF1888tg2NNhIxsc+Y32Z5pZCVBIypQPw3hopAT7yUZrxJSFmbAnGV9I+4nABxGiCWvIZGlLv//n97+nFxz725cykiR2A+5qlAmjrFMRwinsBzYyPfeL5CMMFEJMRisniwQfVAuiJJ+jFxz725eMzrQPwW9vU+4COPE8vPvYJ56MMF0CMQrLT9FSTxQ03qAXQJZfQiy/ed+yxNrz++o04MpLhEohJ8bHPWJ/WS2DRPlM6AI8dUgug19voxZeE6LGlGF8p+6jDBRCjEH+jJuXk8/WvSx65ESK1+OJ9K1d6cd++DDfBmhgf+4z15fyAQaE7AEcm1V5AfdfSiy8JaR8wIBBfqfpEgAsgJinUk8/736+eAaIYH/vYp5uv0B2Abf9E9Fnpxsc+8j5R4AKIUZCb9bndPtLJ4p131OLnnHPoxZfMl02TSTPiY5+xvnT7X5OPegdgE31utw+vuOIgzpjhIxlfqflEggsgRkH+uobGRivp5CN/ASoA4u7d9OJL5sv2a0YKHR/7jPWl2v9Z+fTu2FskvsZGK15/vXTcohhfKflEgwsgJgYKyUKLT/6Tqa2KWfGxj30JUOzYm87ndeP3GrL8ZaiQ8bGPlE9EuABiYiCTLNL4Tj5Z+vuUU2jGxz72JYVqx954XziAkV3n4uTmMvxd43/Ti499tHwCwwUQo2C32/GnP23Fw4fteXmMTj6LFkn/vvhimvEl89ntdmxtbUW7PfPYkkze7MvLZ7fbsfXWn6L9rwtoduyN871+2xLELYCv/OGzJOOL9h0+rH1umRFfsftEhlz0XACZh8fjwe7uHvR4PDk7jE4+Vqt6+eumm+jFl8rn8Xiwpyfz2FJM3uzL3+fxeLDniW50d62k27E3yvfGb6sQtwBG7pstPRpPLL5on9a5ZVZ8Re8TGC6AGN0oRPLp6VELoIceohcf+9iXzke5Y2+8T/kz9h+S8bGPhk9kuABiFPx+P/b396Pf78963UIln9/+Vi2A3n2XXnypyDS2ZsfHPmN94+N+vPzyfpw2zU+yY2+Cr2upWgC9uZlefFE+/8R41setQsZX7D6RIRc+F0DmMTo6ijfeeCOOjo5mtV4hk09jo/T68cdL3whPLb5UpBtbCvGxz1hfY6O0/xsbRyUfoY69SX2BMcQtZZLvoQ/Siy/KN7qnMavjVqHjK3afyHABxChEIhEMBoMY0VpZYOGTz9lnS+995Ss040tFqrGlEh/7jPWVlUWwvj6IgUDU/ifQsTetb9dH1LNAFOM76otsLsPgk/UYCQdIxlcyPgHhAojJmUInH68Xsbxcev+66+jFxz72Ze2j3AH42e9Innun0YyPffR8gsEFEKPgcDiwo6MDHQ5HxmXNSBb796v3/9x/P7340hE/ttTiY5+xPqs1zdyi2gH4td8fLYCmIj75DXrxHcVxxIodv7sWHXdZSMZXcj6B4AKIUXC73bh79250u91plzMr+bS2qgXQG2/Qiy8d0WNLMT72GevLOLcodgCeeA/R8S/EcIBmfEeRxnYXurtXkYyvJH2CwAUQkxVmJp8rrpCWmzcv9Q3QIiZH9rEPEcXrAMw+9gkOF0CMQiAQwKGhIQwEkt9MaHayqK6Wlv385/Xx6R1fOgKBAA4MDGF9fYBkfOwz1pdpbikI1AGYii9mbAnGV9I+4nABxChQflQ7EECcNk1a/ppr8vfpHV8mhoelsa2sHCUZH/uM9WXVYoJaB+BIBNEzRDa+hLElFl/J+wjDBRCjEA6H0e12YzgcjnmdQvL517/U+3/uuSd/n97xZfaFcc4cN9bVhYnGxz4jfanmViofmQ7Ab25F7JwvXRbxjdKLDxPHllp8pe6jDBdATFqoJJ+ODrUAiv5oUImPfezT20eiA/B7j6u9gIYfpRcf+0j7qMMFEKPgdDpx69at6HQ6EZFWsviv/5LWmzkTMRTK36d3fJl8q1c7cfNmdWypxcc+Y33xc0uzz+wOwD6bUgCFX7yZXnyojq3N5iQZX6n6RIALIEZhbGwMd+zYgWNjY+SSz/nnS+t+6lP6+PSOL5PvyBF1bCnGxz5jfdFzK2uf2R2Ad56CuAXw2Q11JOMbGxvD7dt34KpVYyTjK0WfKHABxCRgdrKIJxxGPOYYaf0f/pBefOxjn+E+EzsAT/ZcjLgF8PXfLCIZH/to+USCCyBGIRQK4fDwKNbXh0gli9deU+//+fOfxUxmoVAIR0dHMSRfvyMWH/uM9YVCIRwdGU7Y/1n59O7Yq9F3389uVC6DBb0ucvHV14ewslI6blEcv1LyiQYXQIyC/DgptUe1t2xRC6CaGvOTWS6+rB6DNiE+9hnrU/b/yHB+Pr079mrwff3jD6k3Qh9+klx8lZXS2A4Pa2sxQLqDssA+EeECiFEIhTT+JqUBPZPP2rWSp6yMRjLLxZftGSBKyZt9+ftCoRCO/qUSQ0/W599hN0kSMtLX9O131QLotd+Si6++XjpzrfnsaoHjK3qfwHABxMRAIVnE88UvqmeAKMbHPvZpgnLH3nS+QARxe6WU8J77Ab342GeqT2TIRc8FkHnIT1Pk+qQSov7JJxBAnDpV8p1+OqFklqUv01NAZsfHPmN9Y2NjuOOB7eh8bBXdjr3pfLb9iN53U38Jn4nxaZ1bZsVX9D6B4QKIUdDSqyQdRiSfiy5Sz/78/vf5+8xKjjn3gSlQfOwz1ud0Sn2gGhtsZDv2iurL9bgl6vZS84kMF0CMLhiZfOQ///gHzfjYx75sfBQ79rKPfbn6RIZc+FwAmUc231cUjZHJ4kMfUm+AHh/P32dWckw3thTiY5/RvrjvgsvjaZqk8RXKF/ZJ3aEJxRd+qg7dLrvm45ap41eEPpHhAohRoPio9oUXSv8+6yx9fGYlx1RjSyU+9hnrS/qodg5JKG18Rvse+wzi1imI/7ySVHyjf6lMaDFAKb6S8QkIF0CMQiAQwKGhIQwEApqWL0Tyed/7pP+vXq2PT+/4tJJsbCnFxz5jffX1ARwYSDK3TOzYm7VPfhJsz2JS8QWerMehO0/FwNEWA7ptL/X9QdEnGFwAMTlRiOTz3nvq/T+33kovPvaxTxefSB2AtwDivdMSnGTiY5/5PoHgAohRcLvduHv3bnS73WmXK1SyeOQRtQDauzd/n97xZUP02FKMj33G+jLOLRE6AD96nloEHTlAJj5pbHehu3sV7fErJZ8gcAHEKDgcDuzo6ECHw5FymUImn1/9Si2A0oRkWnzZII+t1eogGR/7jPVpmVvkOwA7/qUWQHuWkIlPGdsjVtrjV2o+AeACiNFMoZPPpZdK751+Os342Mc+3X1RSYhcB+DJEOK2mUoRRC4+9tHzEYcLIEYhEolgMBjESJJur2Yki9NPl95fvlwfn97xZUMgEMH6+iCWlUVIxsc+Y33p5lYCRnXs1cP36KcQtwBGOo8lE1/C2FIev1L0EYYLIEaB0qPadrt6+Wv9+vx9eseXra+xURrbxsZRkvGxz1hfNi0mgkHCHYCf/yHiFkDf3bPw/qtrScQXP7akx68EfZThAohR8Pv92N/fj36/X3nNrOTT3a0WQI88kr9P7/iy9U2b5sfLL+/H8XF/5pVMiI99xvqSza1MPoodgEP9d0jNB+8pxx9/t59EfNFjS338Ss1HHS6AmJSYmXxuvVUtgEZG6MXHPvYVxEesA/AVjYfxvEX/xIaVXpLxsY+OTwS4AGIUPB4P9vT0oMfjMT1ZrFolLXviifr49I4vW5/TqY4txfjYZ6wvem5l7aPeAdhkn8fjwe7uHly50kMyvlL0iQIXQIyC3W7H1tZWPHzYbnryOessafmLLtLHp3d82frksbXb7STjY5+xvnT7X5OPegdgE32HD9vxpz9txQUL7CTjKzWfSHABxMRAIVmMj0tffgqA+LOf0YuPfezLyUetY28+vkgE0Tss/U0xPvaZ4hMNLoCYGCgki3371Pt/duzI36d3fOxjX04+ih17c/G983fEB06Q1hl/k1587DPFJyJcADEKVqsVr79+PTY2Wk1NFn/6k1oAvflm/j6948vFZ7Vacf369Wi1WknGxz5jfVarFdffdD1a9zTm32E3SRIqqM/6D7Uj9NsPmB5fY2N2c8v08Ss2n8BwAcQo+Hw+vOKKg+h2+/Ly5JssLr9cWnf+fOkMO7VklovP5/PhwYMH0efLPLbFsL3si8Xn8+HB+69A399m0OzYm40vOI64pUxKgAd/llRXyPjcbu1zy4z4it0nMuSi5wLIXCgki3PPldb/0pdoJjP2sS8nJol37M3G9/BZUvLruYhmfOwrnE9guABiFLxeL+7fvx+9Xm9O6+uRLPx+xIoKyfE//0M4mWXp0zK2pJM3+/Lyeb1e3LdvP65e5SLbsTcr376j37z+wPtMjy/X45ap41dEPpHhAohRsNlsuHHjRrTZbFmvq1eyOHBAvf/nk5+kmcxy8WUaW7PjY5+xvpERG15//UY89lgbyY69WftevVW9D2jiPVPjs42OZH3cMn38isgnMuTC5wJIPPRMFm1tagFENZmxj315+/J4moaE7/ATagH07i568bGvYD6R4QKIyQu9k8WVV6rFT22tIMmMfezLxZdDEiLjCzjVAuilm+jFx77C+wSECyBGIdtLYEYki/nzJd9xxwmWzDKQbGwpxcc+Y30NDSnmVhZJSFN8hfT9fdHR11eYGp/t0Qbc+Ku1aHu0QazxK0afYHABxChkczOhEcniW99Sz/5cfXX+PkrJMX5sqcXHPmN9LleauaUhCWUVX6F8tv2I44OIkYip8XnHXbh/yxr0/m2mWONXrD6B4AKIyRojk4X8529/oxkf+9hniC9NEmIf+4TzCQIXQIyClmZ9RiaLJUvUAijX3U8imSVBHlu320cyPvYZ69PUCNPojr1F6lPG1usmGV/J+gSACyBGIdPXNRidLH74Q+nfM2cihkL5+yglR3lsGxutJONjn7E+zV+FEpWEqHcAln1TpwTwew05fn2ODvHFjK2A41fUPuJwAcRoohDJ4tOflv7/6U/TjI997CuIb1KQDsBbAN/900kY/Ns0nHzyWyTjYx8BH2G4AGIyUohkEQ5LZ34AEH/0I3rxsY99hfSJ0AFY9uEWQHxwIdn42GeujzJcADEKdrsdW1tb0W63K68VKlm8/LJ6/09HR/4+vePL17dggR1/+tNWPHzYnnklE+Jjn7G+ZHMrk496B+CV9UGcfPjDahHkGzUlvuixFW78itxHHS6AGAWPx4M9PT3o8XgQsbDJ4m9/Uwuggwfz9+kdX76+lSs92N2tji21+NhnrC9+bmn2Ee4AHAwi4jt/Vwugrs+ZEp88tk6nR7zxK2KfCHABxCSl0MniJz+R3ps2TdvPKqbkyD72pfVR7gA8MaIWQBTjY58pPlHgAohR8Pv92N/fj+Pj/oIni89+Vnp/yRJ9fHrHl69PHlu/308yPvYZ60u3/zX5KHcA3nGS5Nl5iinxjY/78fLL+3HaNL+Y41dkPpHgAohRGB0dxRtvvBEbG0cLmiwmJxFnz5aWaWrK36d3fHr45LEdHc3uPglRt5d9saTa/1n5qHYA7vm65HjoDFPia2xUj1tCjl8R+USDCyBGIRKJYH19EMvKIgVNFocOqff/tLXl79M7Pj18kUgEg8EgRiIRkvGxz1hfJBLB4JP1GAkH8vNR7AD84g3qJTD/kYLHV1YmHbcCAQ1zi+L4FYlPRLgAYmIwI1ls3aoWQAcO5O/TOz72sU8XH9WOvfn6om+Etu2nFx/7jPUJDBdAjILD4cBrr+1Aq9WRlyfbyfX//p+0bEUFYrJvCiCZzLL0ORwO7OjoQIcj89gWw/ayLxaHw4Edv7sWHXdZaHbszccXcCKOPoUYHDMlPqtV+9wyI75i94kMuei5ADIPt9uNu3btRrfbnbMjl2TxpS9Jy3/sY/r49I5PD5/b7cbduzOPbbFsL/tikfb/LnQ9vopux15BfVrnllnxFb1PYLgAYnQjl2QRiSBaLNI63/te/j6942Mf+/T0Ue7Yyz725eITGS6AGIVAIIBDQ0MYCAQyLxxHrsliYEC9/+e22/L36R2fXr5MY2t2fOwz1ufxBPCyy4Zw6tQAyY69uvl8NsRwoKDxBXyerI9bZMdPQJ/IkAufCyDzMONR7c5OtQDavz9/n97x6eVLN7YU4mOfsb6ER7UJdezVxWd9VukDFHpvX0HjG93TmNVxi+T4CewTGS6AGIVwOIxutxvD4bDmdfJNFj/9qbRueTmi1ytGMsvFl2psqcTHPmN95eVhXLXKjT5f1P4n0LFXN9/4m8qTYB3r/ljQ+MKby9HdvQrDwSRPUBi1vezTZX6YDRdATM7oMRm+/GVp/Q9/WJxkxj726eYrlg7AkQhGOhcgbgH8S9N36cXHvsL4BIMLIEbB6XTi1q1b0el0ZlxWj8kQiSAed5zkaGwsgmSWhvixpRYf+4z12Wxp5lYRdAAOBhFf/F0N4hbAt24/p6DxOe023HrbWnTeNU/o8Ssan0BwAcQojI2N4Y4dO3BsbCztcnpNhqEh9f6fj31MnGSWiy96bCnGxz5jfRnnlsAdgGXfr1c2I24BjGydghiaKFh8Y2NjuOOB7TjWtUro8SsqnyBwAcRkhZ6TYedOtQASKZmxj32G+ETrABzn++1/3xfVEfo5cvGxr8A+AeACiFEIhUI4OjqKoVAo6ft6T4b//V+1+LnkkiJLZnGEQiEcHh7F+voQyfjYZ6wv09xSiEpC1DsAJ/gcr6sF0H9uL1h8MWMr8vgVo484XAAxCoV+VPt975N8c+aIlcxyYXhYGtvKylGS8bHPWF9WLSYmBekAHO+LTCLeP1cqgP55RcHiSxhbUcevWH2E4QKIUUj1W6qRyQIAccUK/XxUk2N9fQgrK6UzQBTjY5+xPs1ngI76ROgAnNTX9QWpAHrs/ILFFz+2Qo9fEfoowwUQkxajix8AxI0bacbHPvaZ6aPeATipz96HONYvnQ2iGB/7CuqjDhdAjEL8kypGHtw/8xm1AHriifx91JPZqlVjuH175ifszIqPfcb6tDxhmdRHuAMwFZ88tkeOjJGMr1R9IsAFEKMQ3avG6GRxww1qAaSh7VBGH/XkmLYPDIH42GesL1OPrbQ+6h2ATfY5nU7cvHkrrl7tJBlfKfpEgQsgJoFCJIuLL5b+v2gRzfjYxz5SPuodgON9IR/ixGG68bHPMJ9IcAHEKITDYbTb3VhXFzb84H7KKeprevj0jk9vXy7fs1bI+NhnrC8cDqPbZc/vu+CodwCO9m2dgvjoeQWKL4xz5kjHraIZP0F9osEFEKMgP05q9KPaVqt6+euWW/L36R2fEb6sHoM2IT72GetT9v/IcH4+6h2AJ4OIfz/jaBFUHtMR2qj4KiulsR0e1tZigPz4CeoTES6AGIVAIICnnjqE9fUBQ5PFo4+qBdDjj+fv0zs+I3yBQACHhoYwEAiQjI99xvoCgQAO3XkqBp6sp9exV2/f63eqDRH3ftnw+OrrAzgwoH1ukR8/0XwCwwUQE0MhksXNN6sFkM2Wv0/v+NjHPkN8lDv26ulzvqQWQBTjY5+uPpEhFz0XQObhdrtx167d6Ha7c3ZoSRbf+pb0/qmn6uPTOz4jfG63G3fvzjy2xbK97ItF2v+70PX4Krode/XyTYYQt82Qvhj16OUwI+PTOre0+vSOr+h9AsMFEKPgcDiwo6MDHQ5HTutrTRYLF0rLXHqpPj694zPCp2Vsi2l72ReLw+HAO+/swG83WMl27NXV9+gnEbcAvvbH8w2PL9fjFunxE8gnMlwAMbqgNVk4HOrlr1/9Kn+f3vGxj31G+yh27NXbF/7nDxG3AHo6jsFV9T5y8bFPP5/IkAufCyDziEQiGAwGMRKJZLVeNsmiu1stgB55JH+f3vEZ5Us3thTiY5/RvghWVASxri4i+Qh17DXCt+n/3aHcAxQ88qqh8UWersOg36v5uCXC+InkExkugBiFQjyqvWGDWgCNjOTv0zs+o3ypxpZKfOwz1pf0UW0CHXuN8n3stD71RuihTkPjG/1LZUKLgUJvL/vEhAsgRsHv92N/fz/6/X5Ny+cyGVaulJY/6SR9fHrHZ5Qv2dhSio99xvpWrPDjK68kmVtF1AE49jJLAEPvdCH67YbH5+9Zgf1tZ6K/Z0XRjJ+wPsHgAojJiVwnwwc+IK3z9a/r49M7Pvaxr+C+IugAzD72iQgXQIyCx+PBnp4e9Hg8aZfLdTKMjamXv264IX+f3vEZ6YseW4rxsc9YX8a5JXAHYLN9Ho8He57oRs/elSTjK0mfIHABxCjY7XZsbW1Fu92ecpl8JsNTT6kF0N//nr9P7/iM9Mlje/iwnWR87DPWp2VuCdcBWKsv4EIMeQ2LTxlb22Ea28s+YeACiNFMvpPht79VC6B33hE3mbGPfYb5opIQ9Q7AGX3jA4h/XyQl1Le20YuPfcb7iMMFEKMJPQ7ujY3S+pWViIEAweTDPvZR8E0K0gE4ky/sk74Vfgsg9l1LLz72FcZHGC6AGAWr1Yrr169Hq9Ua87peB/ezz5YcX/kK4eRjkK+x0YrXX78eGxutJONjn7G+VHMrlU+EDsCafI98VCqAupcaFl/82BbV+BWBjzJcADEKPp8PDx48iD6fT3lNr2Th8SCWl0ues84SO5nl4psxw4dXXHEQ3W5f5pVMiI99xvqSza1MPuodgDX59l8uvd85HzFDE9Bc44seW9O3l31CwQUQkxI9k8U//qHe/yN6MmMf+wrmI9wBWJOv/09qQ8TxQXrxsc8wnwhwAcQoeL1e3L9/P3q9Xt0P7r//vVr8XHSRIMlHR5/LpY4txfjYZ6wvem5l7aPeATidz/psVEfo7YbE5/V6cd++/bhypdf87WWfUHABxCjYbDbcuHEjjozYdE8Wp58u+aZOlW6AztcnWnKUx9Zms5GMj33G+tLtf00+6h2AU/lCHsSt5dLr/7rOkPhGRmx4/fUb8dhjbeZvL/uEggsgJgYjkwUA4pe+RDc+9rHPUB+1jr2F8u06W3rtia/QjI99uvlEgwsgJgajkkVZmfR3c7M+PtOTGfvYl62PYsfeQvj+8W3ELYCRHafQjI99uvhEhAsgRsFms+HatRuxocGma7JYulQ9A7QtRT+0bHwkklmWvmwvgYm+veyLxWaz4cZfrUXbow2Ik8Q69hrtcxzEoO2l3JvqZYivoSHLy8uijR91n8BwAcQoeL1eXLNmP7pc3rw88ZPrttvUAug//8nfZ3Yyy8WX6SZYs+Njn7E+r9eL+7esQe/fZtLs2CuwL6cHDATeXmo+kSEXPRdA5mJEsrjySun/c+ciTk7m79M7PvaxryC+SeIde9nHvlx8AsMFEKOgtVlbKlIli+pq6bXPf14fX66Y6dMytsW0veyLxefzYW/vQVy90k22Y6/hvidrEb3v6B5frsct4caPqE9kuABiFLJp1x9PqmQRCEiPvgMgXnNN/r5cMduXaWzNjo99xvqGh614ww3rsbLSSrJjr+G+hz8s+bbN0D0+6+HhrI9bwo0fYZ/IkAufCyDxSJcs+vrU+382b87fp3d87GOfKb48nqYR0vfyLWpDxCe/QS8+9uXsExkugJi8yJQs7rhDLYBefTV/n97xsY99pvlySELC+ka61QKIYnzsy98nIFwAMQp2ux1bW1vRbrdrWl7LZPjhD6X3Z81CDIfz92UDJV+ysaUUH/uM9TU02PG225LMLRM79hbUF3Coxc+uD+san/2xBmy9+Sq0P9ZAZ3tL1ScYXAAxCh6PB3t6etDj8WRcVutk+OQnpWU+8xl9fFqh5osfW2rxsc9Yn9OZZm4VQQdgTb4HFx69BPZNXePzuJ3Yc/dV6Ll7Fq3tLVWfQHABxGSN1skQCiHOmCEtd/XV+fv0jo997CPjE7gDsGbf09+S1t95Ks342KefTxC4AGIU/H4/9vf3o9/vT7lMNpPhpZfU+3/++tf8fVqg6pPHdnzcTzI+9hnr0zK3hOsAnK3v5ZvVy2A+m27xKWM7MU5re0vdJwBcADEKo6OjeOONN+Lo6GjS97OdDHfdpRZAL76Yvy8TlH3y2DY2jpKMj33G+jLNLYWoJES9A3DWvuFH1QLovcd0iy9mbCltL/vIwwUQoxCJRDAYDGIkEkl4L5dk8eMfS8vPmCFdDsvXlw7qvkAggvX1QSwri5CMj33G+tLNrQQmBekAnK3PZ1ULoNd+p1t8CWNLZXvZRx4ugJiM5JosPvMZaZ3zztPHp3d87GMfVZ8IHYBz8r33OOLECN342Ke7jzJcADEKDocDOzo60OFwKK/lenAPh6VH3wEQf/AD9XURko8RPovFgdde24FWqyPzSibExz5jfcnmViYf9Q7AVHzRY0sxvlL2UYcLIEbB7Xbj7t270e12I2J+yeK119T7f+64A/P2JUMk36pVbty1Sx1bavGxz1hf/NzS7CPcAZiKTx5bh8NNMr5S9YkAF0BMUvJNFps3qwVQb29xJTP2sa+gPuodgHPxTXjwO6vG6MbHvrx8osAFEKMQCARwaGgIPZ5A3gf3//kfaf2pUxHHxwVOPjr55LENBAIk42Ofsb50+1+Tj3oHYK2+oBsjD5+Nk5vLce1Ft+oSn8cTwMsuG8KpUwP0trcEfSLBBRCjoOej2l/4gjShPv5x85MPBZ/mx6BNio99xvpS7f+sfHp37DXJZ7/zRMQtgPtaVuria2xUj1sUt7eUfKLBBRCjEA6HcdUqN5aXh/NKFpOTiHPnSpNq4ULzkw8FXzgcRrfbjeFMX4hmUnzsM9YXDofR3b0Kw0Fffj69O/aa4Hv4/30NcQtg5O9n6uIrL5eOWz6fhrlVBONH1SciXAAxMeiRLF5/Xb3/h0LyYR/7SPioduwtsO/+n9+g9gMKjpGLj31Z+gSGCyBGwel04tq1W9Fmc+bl2bJFLX6WLiWSfEz2OZ1O3Lp1KzqdTpLxsc9Yn9PpxK23rUXnXfNoduwtoC/05oNqATT6VN4+m0373DJje4vdJzLkoucCyDzGxsZw+/YdODam4beyFASDiB/8oJQsysoQ81ApPmrJLBff2NgY7tiReWyLZXvZF8vY2BjueGA7Oh9bRbdjb6F8nreTd4TO0ad1bmmOL1dK1ScwXAAxuhGdLAAQP/IR/XyUkhn72Jerj3LH3oL5IhHE7ZVSAfSPb9OLj31Z+USGCyBGIRQK4ejoKIbiv7hLA9HJYto06e/vfjf3WERIZtn4Mo2t2fGxz1jfxEQIGxtHsaIiRLJjb8F9T3xF+v+us/P2hQITWR+3hB8/Qj6RIRc+F0Dmocej2hddpJ4B+uMfc4uDejLLxZdubCnExz5jfQmPaufxNE3S+ETz9TVL/753OmLYl5dvdE9jVsetohg/Qj6R4QKIUcjlDFD8ZLjvPrUA2rcv+xhESGa5+FKNLZX42Gesr6JCOgM0MRG1/wl07DXN13Mhom0/Ytifty90TwWO7mnEUGCC7vaWgk9AuABicibZZLj+evUGaI8nf5/e8bGPfaR8RdQBmH3sEw0ugBiFbJ6mSDUZvvpV6bUPfSi7n10UySwN8WNLLT72Ges7ciTN3CqCDsBm+sacR3DHn3+CY3fNJRlfyfkEggsgRkFrr5pUkyESQayslF5vaND+c0VLZrn4oseWYnzsM9aXcW4J3AE4b9+T38jL53Q6ceuWzeh8fLUY21sKPkHgAojJinST4e231ft/fvvb/H16x8c+9pH2idYBOF/ff/6MeO80yff0cnrxsa+oix9ELoCYKDJ9X1WmybBzp1oAPaWhwSup5GOwLxwOo93uxrq6MMn42GesT/N3wUUlIeodgPP2vbVNbYiYhy9mbClvbyn6iMMFEKOQ76PaP/uZegO0253+Z4mczHJheFga28rKUZLxsc9YX1YtJozq2EvNN/Yftfh5pDpnX8LYUt3eUvURhgsgRiEQCODQ0BAGAoGY17UmiwsvlJb54AfT/xzRk1kuvvr6AJ566hDW1wdIxsc+Y32p5lYqnwgdgPP2RSYR75+LuAWw+/++n7MvfmzJbm+J+ijDBRCTFq3JIhJBPP54abnVq/P36R0f+9gnmo96B2A9fJOPfx5xC+CBXy0mGR/78vNRhwsgRsHtduPu3bvRffT6VTYH93feUe//+c1vki8jUvLR27dqlRt37VLHllp87DPWFz+3NPsIdwDWw/fwTdcgbgEM/m0aBv2BnHzy2DocbvLbW0o+EeACiFFwOBzY0dGBDocj62Tx4INqAfTkk4nvF1Myy8VntapjSzE+9hnri55bWfuodwDOw9fwmXvUG6HtvTn5HA4H3nlnB65e7SC/vaXiEwUugJgEckkWP/+5WgDF93ozO/mwj33C+6h3AM7R95PLX1ULoNfbycXHvux9IsEFEKMQiUTQ6w1iXV0k64O7/CWoZ54Z+3pRJB8dfJFIBIPBIEYiEZLxsc9YXyQSwaDfm7D/s/JR7wCciy8QRrxvlrTOCz/J0RfBigrpuEV+e4vcJxpcADEK8uOk2T6qHYkgnnCCfK+L+jqV5EPBl9Vj0CbExz5jfcr+HxnOz0e9A3AuvsM9iJ4h6UCSg6+yUhrb4WFtLQZM394i9YkIF0CMgt/vxzPP7McVK/xZHdzffVe9/LVxo/QapeRDwef3+7G/vx/9/vTffm1WfOwz1uf3+7G/7Uz096yg17FXcN+KFX585RXtc0v07SXnExgugJgYcjm4P/SQWgD19NBLPuxjHwkf5Y697GNfjj6RIRc9F0Dm4fF4sLu7Bz0eT1br/eIXagFksxFNPib7PB4P9vRkHtti2V72xeLxeLDniW50d62k27HXbF/Yj+i3Z+3TOrfyjo99yX0CwwUQo2C327G1tRXt9jQHoSR8/etSgjjjDJrJh4JPy9gW0/ayLxa73Y633daK3244TLZjr2m+SATx8QsQt1YgPv/DrH25HreKZvxM9okMF0BM3px4opQkTjmFZvJhH/so+Sh27DXd99j50vuPnkczPval9IkMufC5ABKL995TL3+JkHzYxz4SvjyepilK34GrpffunZ7wHon42JfSJzJcADEKVqsV169fj1arVfM6O3eqxc/nPy9I8jHBl2psqcTHPmN9lZVWvOGG9Tg8HLX/c0hCaeMT2Tdwt9oQ0XEwK5/1L5W4/pc3oPXwsDjbW4w+AeECiFHw+Xx48OBB9Pl8mpYPBhHPPjv2Buh8ECWZ5eJLNraU4mOfsb4VK3zY25tkbmWRhDTFJ6rP+bJaAL3RkZXP98QKPLjpXPQ9sUKc7S1Wn2BwAcTkRPRkkG+A1stHPZmxj326+jQkoaL3TYYRtx0jvfb8VfTiY1/RFT+IXAAxUXi9Xty/fz96vd60y0VPhhkz5N9wc/+5RZXMUhA9thTjY5+xvoxzK00Syik+EX2PnS89nbTnvKx8Xq8X9z+7D71PrBRre4vZJwhcADEKNpsNN27ciLY017KiJ8M3vqGeAdqwIbefKWIyywV5bEdGbCTjY5+xPi1zy/COvdR9z/8IcQtg4G/TsWJKULNPGdvREbG2t9h9AsAFEKOZ+Mnw4INqAbR3b/4+UZIZ+9hnmC8qCVHvAKy7b/cnELcAhu8px/++/DV68bGv6OACiNFEsoP7TTepBZDTmb9P7/jYxz4hfZOCdAA2wNd93RfxslVOsvGxr7jgAohRSHWaPtXB/ZvflF5btCi7n0M6+Rjka2iw4dq1G7GhwUYyPvYZ69N0CSzKJ0IHYCq++LGlFl+p+yjDBRCjkOxGzXQH9/e/X31dK8WQzHLxzZzpxTVr9qPL5c28kgnxsc9YXy4PGFDvAEzFl+oBAyrxlbKPOlwAMSlJd3A/fFi9/PXrX+fv0zs+9rGvKHyEOwAb7nu6lnZ87BMeLoAYhehmfZkO7o88ohZAXV2Z3UImHx19bnd2TSYLHR/7jPVlajKa1ke9A7CevpEuxAdOVJsiavD5fFKTyRUrfOJtb5H6RIELIEZB/rqG4WFrxoP7L3+pFkCZvoTZ7ORDwZfL14wUMj72GetLt/81+ah3ANbL53xJLX72LNHkGx6WvmakstIq3vYWoU8kuABiYtB6cL/4YmmZhQv18ekdH/vYR85HrWMvRd9kCHHbzKMdoX9ILz72FVURxAUQE4PWyXDyydJytbWplyGVfNjHPrN9FDv2UvQ9+ilp/Uc/RTM+9hVNEcQFEKNgt9vxqqtasaHBnnYyjI6ql79uuSX5MuSSj8k+u92Ora2taM90vdCk+NhnrM9ut2PrzVeh/bGG/Dvs6t2xl5rvaEdo3DZTOiOUwdfQYMfbbtM+t8htr+g+geECiFHweDx41VU96HR60i63e7daAD3+eOL71JIPBZ/H48Genh70eNKPrVnxsc9Yn8fjwZ67r0LP3bNoduyl5HvjL+p9QM6XMvqcTu1zi+T2Cu4TGXLRcwFkLlomw69+pRZAR44krk8t+bCPfSR8k8Q79lLxOV5UC6CBv9KLj32xPoHhAohR8Pv92N/fj36/P+1yl1wiJYTTT499nXTyMdmnZWyLaXvZF4vf78dXXunH1SvHyXbsJeObDCFumyEVQAf+K6NP63FLt/jYF+MTGS6AGIXR0VG88cYbcXR0NO1yp5wiJYVvfUt9jXLyoeDLNLZmx8c+Y33Dw9L+r6wcJdmxl5zv0U9Ky3cvzegbHRnWdNwivb0C+0SGXPhcAJlHJBLBYDCIkUgk5TJWq3r56+abpdeoJx8KvnRjSyE+9hnti2BFRRDr6iKSL4+naZLGV2w++wuIY/9BjExm9EWersOg35v2uEV+ewX2iQwXQExW7NmjFkCPPSZK8mEf+wj6ckhC7GMfWZ+AcAHEKDgcDuzo6ECHw5FymfXr1QJoZETg5FNgX7KxpRQf+4z1rV7twDvvTDK3skhCmuIrQZ/j8dXY8evvoePx1STjKymfYHABxCi43W7cvXs3ut3ulMtceqk0AU47TZzkQ8EXP7bU4mOfsT6HI83c0pCEsoqviHwVU4J4ReNIWp/b5cDdd16N7rtnC7+9ReETCC6AmKw49VRpErz//eIkH/axj7yvgB17RfHdccX3MfC3GTj56Pkk42Of+HABxCgEAgEcGhrCQCCQ9H2bTb38VVTJpwA+eWw9ngDJ+NhnrC/T3EJE4zv2CuZ79Fc/PNoR+hjEyXBKjTK2Po/Q21t0PgHgAohRyPSo9q5davHz2c+Kk3wo+OSxbWwcJRkf+4z1aW0xEZ2EqHcANtoXOnRnVEfo1PkgZmwF3t6i9BGHCyBGIRwOo9vtxnA48betYBDxIx9RC6D33svvZ4mczHLB5wvjqlVuLC8Pk4yPfcb60s2tBCYF6QBstM/xr6iO0HenXD1hbEXd3mL1EYYLICYj0Qd3AOk+IL18IiYz9rHPaJ8IHYAN900GEe+dfrQj9I/pxcc+4eECiFFwOp24detWdDqdymvRB/djjpH+vvTS3H+GCMnHCN+8eU5cu3Yr2mxOkvGxz1hfsrmVyUe9A3BBfHs+If3/8QtSOqLHVvjtLTIfdbgAYhTGxsZwx44dODY2hoixk+Gb31TPAK1fn5u/WJJZLr5Vq8Zw+3Z1bKnFxz5jffFzS7NPx469Qvqea5L+fd+slDdCy2N75MiY+NtbRD4R4AKISUr8ZHjkEbUA2rMnf5/IyYx97Cuoj3oHYCN9exar9wG5XqUXH/uEhgsgRiEUCuHo6ChOTIQSJsPNN6sFkNWanVfo5KOTTx7bUChEMj72GetLt/81+ah3ADbSJ/95+4GkvomJEDY2jmJFRag4tldwn0hwAcQopHtUu7ZWmiCnnJKdk0LyoeDT/Bi0SfGxz1hfqv2flY96B2AjfE9/S/I9dUlKX2OjetwSfnsF94kGF0CMQiiU+jephQulSXLJJdp9VJIPBV8uZ4BE3l72xRIKhXB0TyOGAhP5+ah3ADbBV1EhHbcmJjTMrSLYXqo+EeECiIkh2WSw29XLX7/6lTYPpeTDPvaR8FHt2Ms+9uXjExgugBiFsbEx/MlPpKcpounqUgug3bsze0gmH5N9Wp4CMjM+9hnrGxsbwx1//gmO3TWXZsdeEXxPXpzUd+SI9rllaHwl6hMZctFzAWQeTqcTN29O7FXy61+rBVCmW1goJh8Kvlz6wIi8veyLxel04tYtm9H+2Gq6HXup+sbfRHxwoZS895yX4NM6twyLr9R9AsMFEJMROQGcfHL65agmH/axj4qPcsdesr5wAPHeaYhbAHfd9N/04itxn8hwAcQopPq+oqoqKQlcfHHqdUVIPmb6Mn0XlNnxsc9YX/R3wVHs2EvdN7lb6gf05M8+l+ALB33av2fNoPhK2Scy5MLnAsg8kj2q63Col79++cvk61FPPhR86R6DpxAf+4z1JTyqTahjbzJfVVUVqfgeXy91hPbeNQeDgckY3+iexqxaTBRi/Arp6+3txZaWFqyurpb2W4HjExkugBiFQCCAQ0NDGAgElNe6u9UC6JFHEtcRIflQ8CUbW0rxsc9Y39SpAbzssiH0eKL2P4GOvcl8bW1tCADYtul2MvFd+cU2tSHiWH+ML3DPVBx6+DIM+DymxWeWTy5+LBYLAkBCAVTQ+ASECyAmLS0tagF0+HDse6IkH/axj6yPYAfgqqoqNZkSia/5ihfUAujNLUl9zt0Xkxg/M3wtLS0JBZAp8QkGF0CMgtvtxt27d6Pb7VZeq6+XPvDvf3/sskWRfAroix9bavGxz1ifw5E4txQIdQDu6upCAFD+dHV10YjP50e8d6q0Tu//xCzrdjlw951X48dOK09ZBBVq/MzyyWft5ALI1PgEggsgRsHhcGBHRwc6HA7ltUWLpA/9N7+pLida8qHgix5bivGxz1hfsrkVA5EOwDU1NTGXVGpqaujEt7taWr7r8zEqh8OBX/3qVxEA0Nlu7viZ5WvbdLtSAJGITxC4AGJS4nSql79uukl6TcTkwz72CeEzuQPwwMCAVEQ4ncolFQDAgYEBEvHhP6+Uln3gfYiRiPKyfPYD4OhlMFE6KOvoa7t2sVIAkYlPALgAYhQikQgGg0GMHD247N2rFkC7dhFLFoL5IpEIer1BrKuLkIyPfcb64udWSkzsANzc3IxNTU2IKDUXlIsK+bV0vgsvHMClS2uwpqYGa2trsaqqCquqqqRLaFEMDAxgTU3ccifNwq7rEuPr7x9QlrFYLFjzhfOxa+cdiJPq4+6dnZ3K2Sq5AKg6aRbWnqd9/JLGJMc+GcTOX34aq09XLwsCOBVfc3Ozcs8UQGw6dTqd2NbWhtXV1djb24sDAwNY+63laJk9FS3HAH7r8yfj8kusMfElXae2Fi0WC1osFqytrY1t+Hh0f7R9X/r5s2ZVKb57740dm+rqauzs7IyJUS50LRaL9F6enz+n04lNTU1YXV2NTU1NWFtbG/O5ogYXQIxC/KPaGzaoBdDQkLjJh4JveFga28rKUZLxsc9YX7o2CAmY1AHYYrFgb2+v8v/a2tqkiT3e9/WL+tX7hY4in03S+tpjv/lcTHy3396GNTU1ytmn3t5epdBoaWmJCee5555TzwA5nVmNn6Y4J4N44I4a5Wcsv8Qa44u+byqa6LNo8mXF2tparFm6VHl91vSp+PWL+hVfynVq1J9vsVjUs3JH49u0VjoDdMLcWTHbK28LAGBbW1vSMaiqqop9L4/PnxynXKQ5nU60WCwJ+4wKXAAxCn6/H/v7+9Hv9yMi4ooV0gH9xBPFTj4UfCtW+PHMM/txxQo/yfjYZ6wvfm5l8hW6A7B81iGa3t7emGScynftqg/GJD2ZlpaWmDMOzc3NSZe7+eYW/Myn71V8j/3mc0mX6+zsTLwsh4ivvvpqTAGUzfiliik69mAQlSIPANC+6+IYX3SREY/8unIv1VHfl5c9h7OmT0UAwKWLj4/xJVsHUdofCfdmHfUtWXz0HqDjE7dXLkrifdGxR5PP5y/ZODQ3NyeceaICF0BMSs44Qy2ARE4+7GOfiL5CdgCuqqpKmqSqq6uVS0upfE1fkpJe87VrY9YdGBiIOaPU1NQkLdfcHBPfhRcOIECv4qs+HbBmyfEJ2xt9Wa75mh/E/Bz5davVmdX4JYspOnZ1e9WfHX+jtZYCSB6H6PE796M3qwXdfRcqvvh1oom/N0v1HX0K7KRZCdsbfYYq5szRUV9tbW3M/sjn8yf/nOjPknwpjyJcADEKHo8He3p60OPxoMulXv4SIVlQ961c6cHubmlsKcbHPmN90XMrK18BOgp3dXWhxWJJ6og+63LvvZ1JfV2/+5x61mLp0pTJLjoR19TUYH//QILPabcqy1SdNEu5H0f5c8oCrDqhDJtqpiJGJhER8d///reyzsUXO7Mav/iYomOPHj/Jm/xGa60FUPz+sFpVZ+ePVV+6Aii6CIzeH4sXRz0Gn2R75UI2/l6c6Pu09Pj8yQWlxWLJ/stpTYALIEbBbrdja2sr2u12fPxxtfj5zGfETj4UfIcPq2NLMT72GeuLnltZ+wzuKFz7reVKUk33x2KpTulr+cE5Mcs2NTUlTYDRZzCkP0148cVOxScXJLVfODn59v5nU1RH6P8gYuyluugblLWOX3xMTU1NMWeS4osVp90a4xt4Xb08Fo/8+nPP9SbdH/IlrbZrFyu+dAVQ9DpS0SP5br89qgBKsr3Rhay8XwYGBpTCV6/Pn3zPT6pLbtTgAohJIBhE/OhH1QLorbfy9xVTMmMf+wruM6ijsLP9aLI98Bw6nc6kf664oinqLElvSl/X7z6HVcdHF0yWpEl8z56uo08rJS4nF0DV1R9Pvr1Hno/qCL0VEaWnxaLPAOUyfl1dXTFPc02dakGAXvXMVNSZF/lGa9k3cN+FGQugmprepPtD/pldj+1RfJkKIDXOLsUX3wgx2fbG30Te3NyMzc3Nun/+oostqvf+yHABxMQQPRkAEN/3Pv18wiYf9rFPDx/BjsLNqz+INR9J75Pu0Tn6BNTy2iSi2Pjavg9omT1VKW5Sxbd4cZtytkBeLvpyUvyZFpwMIoZ9iFsrpPtwetcmxGe1OvMav9tvbzta/EhFkDx+CQVQlG/gd2rREo96Zqk36f5IVlQpBdCB5xJ8waC6TnSxl1AAJdleuVmiPNZVVVVJL0PmMn7xZ/vkG6/jb6ynBhdAjILVasUbbliPlZVWnD1bmhRf+1ruPlLJx2Sf1WrF9evXo9VqJRkf+4z1Wa1WXP/LG9B6eDg/n84dey0WC3b+8tMZfdLlr7gC4CjJ+gRFFwXyfTVXXNGUEF90wSMvF30pKun27v6Y1Ofn8S9gXR3iggXPp4wt0/g1XXlFku1NjCm6AIp/BL33TvUR9fjxS3fmTN72mCIh+h6gO2sSfHKxZ7FUx/iSFkBJtlcuOJuamvDjH6/W5fM3MDCQcBN59GVJypCLjgsg8/D5fHjuuQfxW9/yKWeAbrghNxel5EPB5/P58ODBg+jz+UjGxz5jfT6fDw9uOhd9T6wg01FYvvdFi0++xwQg8Ymppqam2EsdR301H1GLgmAQsaqqCQE6E+KTzxbIyI+mKz8rKr7eO2uw6v1zsOs6QO9dc7GsbBIvvXQk6WWjlMVQlK/pm1XYed+9Cdu7dGlNQvKWf0Z8P52mK69QC7Cj30Wm+qTX9+yJbQgZvZ3xl7rkdbquS7Y/pHWeey52nZQFUNz2Rt+rFX0PUfznpbm5GWtra5Pf0B73eenq6kp6pidlPITgAoiJoa4OsatLvQT2979n76CWfNjHPhI+kzo8J0O+WVW5RJXBF/8lqdHFRVNTU9LmfJbZU7H569G+Jpw61YL9/bFJ1WKxxBRVTqdTeWpJ/lNVVaVcVrvvuoXKfUBXf/c/R4urKuVsSlub1EQx7dmgo9vb9CXpcp3U50fd3viY5O2U46mtrcWmpiblKSrl9fMAD9xRo4xfdPzRhY5csCRrTqisc9Is7F0fPX7SOrffnriOXEylepovev+q4+pM+nmJ/mqRlAVM9NdwHF0+ugiWxyRV80UqcAHEKHi9Xty3bz/eeqtXKYDefTc7B9nkY7LP6/Xi/v370ev1koyPfcb6vF4v7n92H453ryx4h+d4Ojs7Y274rampUb4GIZkv/use5EQrJzf5bIHctbi2tlYqRDbdHuM764NrcfnyJMulSJLNzc0xfYhqv7UcX7/3QvUm6C2AoTfuRa/Xi3fddRcuXLhQcWvqOzMZxGtXfRBrz5M6Mp9y8nJcvjxzTNFfSyH/nKqqKmy+di0euKMmZvyibwauqalRv9ajpiblTc7KOvfdi0sXH49Vx0vxnXDC0oQzP729vTFnzOTiLOnNx0f3b+15gJ884+SUn5eBgYGY/Z3uTNrkU3XY0tKifN2F/Heyr92gCBdAjILNZsONGzfid75jQwDEE06I+c7BjFBNPhR88tjabDaS8bHPWJ8ytxpHCtrhuRh9nT9erhZBL96gaW4VMr5oX6YnupIhr/Pcc72GxHfCXPW71zL5Up5RivKJDBdATAJnnikd9C+6SPs6lJMP+9hHyVfIDs9F63v8i7Tjeyr2clMuBZD86Ly+8fXirFlVmnxdXV0pv8Q0entFhlz4XACZy9iYev/Pz3+ubR2Rkg/72EfCV4AOz+wz35fukfZUJH10Xqf4Tj65Fm++uSWjr7e3N2Ujw/jtFZm8C6CWlhasqanBmpoarK6uTtkBVCtcAJmHzWbDm2/eiMceK10Ce/DBzOsImXxM8KU6TU8lPvYZ6zv2WBtef/1GHBmJ2v8Gd3guFZ/tr8fixpuvR9voCLn4lCe6fvc5zT55nc99rivv+G6+uUV5+u6DH2yOvaSVxpfq29v1nh9mk1cBVF1djVVVVTEFj3wzXDan/KLhAsg8vF4v3nLLfpw5U7oJ+p130i8vSvKh4Et2EzSl+Nhn8GWvldIDBgk3wRvU4blkfH47ersuxf23fRK9T6wkFV/0V2zUnqc+Ip/Od8456jrLl9cmnkzIIr7oDtlKIdYV9zi+3vtDMHIugGpra1Ne24x5vDJLuAAyl9WrpQ/48cenvwFapOTDPvaR9hnQ4bnofZNhxF0fRtxShtj7P+Tik59wk/58HKvPtGD16Ue/8yuF7+STaxGgGi2Wavz4x6uV9ROeSMsqvmacOtUS86WnRmyvqORUAMldHlP1CJD7JeTSA4ALIPPw+Xz4la8cxBkzfHjhhamXK6rkUyBfdCNEivGxz1hfxkaYOnd4LgnfIx9F3ALoe3QZHuzrlZpMUoqvlH2CkFMBJJ/9SXWHuPxlaLl0geQCyDzefNOK118vfRXGz36WfBkRkw8Fn/xVGMPDVpLxsc9Yn6avQtGpw3PJ+PZfjrgF0HpPlTS2h4dpxVfivlAohDfffDM+/fTTWQRRWHIqgOTvE0l1hifZ97tohQsg83jqKfUJsJ07E98XNfmwj33C+PLs8FxSvkO3qf2A3G/Qi6/EfU899ZRSByxdupRkIZR1ARRd3KS6phj9xXHZdoPkAsg8br1VLYDefjv2PZLJgn3sK0Zfio7M7IvDtl8tgN7cSi++Evc98cQTCTdhUyuEyhARIQu6u7th2bJlAADQ29sL1dXVSZcrKysDAICWlhZobm5OuozVagWbzRbz2htvvAGXXHIJnH76PyAQOB8AACwWG0xMzIFgcAZMnz4BM2ZMwNjYcQAAMG/eEfD7j4FA4BiYNs0PxxwzDi5XJQAAzJ3rgGBwOvj9s2Dq1ADMnj0GTmclAJTBnDlOmJysgImJOVBREYQ5c1wwNnYsRCJTYPZsFyCWg9c7F6ZMCcPcuQ5wuxfA5GQFzJrlhrKyCHg8Figvn4R58+wwPm6BcHgaHHPMOEyZEobx8fkAgDB/vg08nnkQCk2HGTO8MG1aANzuBYS3KQKXXnovHDz4ZXjiidNg+vRpMD4+Dn5/GH70o/mwfTtCQ4MNNm2aB7NnTwev1wuBQAAWLJC2yWazwZw5c2DGjBkwMTEBExMTcNxx0jYdOXIEjjnmGDjmmGNgfNwPV101Dps3V0JdHcCf/uSA2bOnw6xZsyAQCMDY2BhUVlZCWVkZOJ1OqKiogDlz5kAwGASXywXHHnssTJkyBVwuF5SXl8PMmXOhsTEMPT0O+OIXF8DmzRXg87khEomAxWKByclJsNvtYLFYYNo0aZvC4TDMnz8fEBFsNhvMmzcPpk+XtsnjCcDVVy+Azk6AxkYb3H77HJgzJ/02+f1+GB8fh8pKaT85HA6YPl3aJo8nAD/+8dswc+bjMDa2Cn73uzKYMSP9Ns2dOxfC4TA4HA5YsGABVFRUgNstbdOsWRZoaJiEJ5+0wxe/aIHNm6eB359+m9LtJ7d7An784+OgsxOgoeEItLYeA/Pmpd+mdPvJ6w3CD37ggq1bj4VvfWsK3H67C6ZNS79N6fbT7NnzoaEB4cknbfClL82De+6ZDsFgbp89v98PDsc4/OQnlUe31wG33TYdLJbcPntz584Fny8MTU0O2LZtAVx6aQVs2uSGKVNitwkR4YEHHoBvfOMbMHv27LT7ac6cBfCdxhBcdNL3oK56O0w77Rvgr74DJvyhrD97gUAA7PYxWHuNBS49oRGWLe6C8pMuhHlL74ZgGLP+7FksFvD7J2FN0yhcaLkGVn7qfhirXAWR6j/A/GMrs/7sTUxMwLx5x0nbe+Ll8K3FO2DGaV+H4JIOGPf603/2FsyFsh2V8ObYcbDL9g1oqHocZi/5BRw5ZimsvWYuXHrCd+DLix+HsvJpMG9WBYRxCjiCc2HBNDdUlE+COzgLIlAGljMugsmP/Ub97I3shPHnrocwToH508YhEgEYPDIfjpv6LlhmuMETWQDB2R+BBZc8pW5T+RjMePpzMBGeDhPhGXDcjDFpP/nnwTEVfjimIgD+yWkw/omHwHLSR+E7jSH42omXw6XVD8Ix0yYhOOV4GAvPhsrpTigrA3AG50BF2STMmToBwXN+A665S9X99Mz/QPl7D8PcaV4IR6aAPTAXyn1umD3lCITKpsFk+QyY/6EVMPnx36XcJkQAW2A+zJvqgelTQuANz4DA1FNhwSVPQigEcMX33oNLTvghXFr9EHjRAr7y9yXfpqprYMa51ybN7z09PfClL30p6XtLly6FX/ziF/DZz3426fuFojzbFVwul/Jvi8WScXm73Z7yvdtvvx0+8pGPxPy55JJLAADgU5/aDSMjACMjAMuXt4PFcghGRgBOOOEl+NrX7lbe+9rX7oYTTngJRkYALJZDsHx5u/Le0qXb4LTTDsDICMCMGW9Bff0msNnCMDICcMEFO+EDH9gHIyMAZWWHob5+E7jdEzAyArBkyR4455y9MDICEAg4oL5+EwQCDhgZATjnnL2wZMkeGBkBcLsnoL5+E5SVHYaREYAPfGAfXHDBThgZAbDZwlBfvwlmzHgLRkYATjvtACxduo30Nrnd0+GNN86AL395K4yOHgYAgH379sHDD++E++8HCATCcMYZm2B4+C0AADhw4ABs27ZN2Z/t7e1w6NAhAAB46aWX4O6771beu/vuu+Gll14CAIDBwUNw1lntgAhw//0ADzywDQ4cOAAAAG+99RZs2rQJwuEwAADs3LkT9u3bBwAAhw8fhk2bNsHExAQAAOzZswf27t0LU6dKRdSPfrQJ/vQnB0ydCrB3717Ys2cPAABMTEzApk2b4PBhdZt27twJAADhcBg2bdoEb72lbtMDD2yD+++XzoWddVY7DA5m3qZDhw5Be3u78t62beo2DQ+/BaedthWWLz8L2tqmw8MPZ94mAOmgv2nTJnA4HACgbtPUqQAdHRPwox9tgltvPQxTp2bepnT7acuWu5Xt/fjH74bXXsu8Ten2k91+GM44YxO4XBNw//0AXV2Ztyndfpo6FWDLljD86Eeb4Kab3oKpU3P/7B06dAj++td2ZXvPP38bHDyY+2cPAGB83HF0Xjjg/vsBnn46cZtcLhecffbZcPDgwYz7aepUgHvvmwpvH3cWHProP6H8c/fDSy+/ltNn76233oI77tgEf9tcBvV/vB92Tv4Gng1/G6B8ak6fPQCAUGgCqhbdAef/v1sBViM8G/427Pz7wzl99u6++25le62nfBxe+sATUP65++HQ64OZP3s4BeDkS2GvvQZmlvtgevBdOHxkDO64YxO03xmC+j/eD3s8a+AJ2wUA/hFwuAOw6T/14HAHAHwjsHf4HNjzzhKAoCv2szfpg30jH4Cdb10A4BuBSZ8Nthyuh9Hw+wAA4AXnR2HboY/HbtMbQwC+EXjJegLcPfA1AN8IgG8E7h74GrxkPQHANwKHjligffNDyva6zzwfDkzUQFnED285Z8Cm/9RDeMIG4BuBnW9dAPtGPgDgG4HDR8Zi99NLU2Hv8DkAPmmb/vx6PWB5AGZO88Ne+1J4dHQpQMiddpvCEzbY9J96eMsxA8A3AgcOnwbbDn0MAACmTgU486y/QlXdjwEA4N+uD6bepnc8kAt79+6Fz33uc1BTUwPPPPNMTg49yPoM0Pbt26Gurg4A0p8Bmj9/PrhcLmhqaoK2traky6Q7A1Rf/w+YN086A1RRYYPJyTmAOAPKyyegvHwCwuHjjr53BCKRYyASOQbKyvwwZco4hMPSbw1TpjgAcTpEIrOgrCwAU6aMHX2vDKZMcQJiBUQic6CsLAhTprggHD4WAKbAlCnS2ZJIZC4AhKGiwgHh8AIAqIDyculsyeSkBQAmoaLCDpOTFkCcBuXl41BWFobJSekMkBT3PECcDuXlXigrC8Dk5ALS23T88ZPwve/Z4eSTtZ0tyfW38FzOLORytiSXM0C8TbxNvE2CbJPfCs7e30LF5BjMmRGB4CnfBlfFB9VtevEOKHf1wtwZEQhPAjgmKmDBMWGomALg9pdDBMvAcsp5MHn6d9RtGnsBxl/9G4QjZTD/mEnpbImnAubNnITpFQjeQDkEyhfAggtuVrdpahBmHLoJJoLlMBEsh+NmS0X0EU8FHDMtAsdMi4A/VAbjp6+FypPPVLfp9VthFtghEC6DMd8UqJwdls4ATUyBinIksU0efxk8/aINppUFAABgzDcFpk+NwIwKhOC8JTDtpORncQ4ePAjXXpv87FA8H/vYx2DdunWwcuVKTcvrRV6XwLq6uqCmpia5+OglsObmZmhpadHs7+vrg8WLF8ODDz4IZ5xxRjahMQzDMAyjI8888wz88Ic/LMjPmpiYgJkzZxbkZwEAVGS7wpIlS5R/R18OS8WiRYuy8v/73/8GAFAuhTEMwzAMQ5e1a9fCBz7wgZjXDh06BL/73e80rX/cccfBlVdeWdDiByCHAshisYDFYgGXy6VcH45ncHBQ+XeqM0SpOPNM6fTg/fffD2effXa24TF5IF9+5LNv+sNjW9rw/jcOHlvzkMd+9erVCbfD9PT0pCyAysrKABGhqqoKfv7zn0NjYyNUVGRdjuRNTj+xvr4e2tvbYWBgIOn7cgFksVigqqoqK/fcuXMBAODss8+GD3/4w7mEx+TJGWecwWNvEDy2pQ3vf+PgsTWP6dOna1pOLnwWLlxoauEjk/VTYAAAa9asAQDpfqBkdHV1AQDAddddl2NYDMMwDMMUA/I9wQsXLoS//vWvcOjQIfjud79ravEDkGMBVF1dDTU1NdDX1xdzuUumvb0dLBYLNDU15R0gwzAMwzDiQq3wkcmpAAIA6OzshOrqaqirq4u5GVo+O7R3715NfYIYhmEYhikuPvWpT8H3vvc9koWPTM7RWCwW6O3thQ0bNkBdXZ1yY3RVVRW8+eabORc/lZWV8Itf/ELp/8AUDh574+CxLW14/xsHj615pBv7mTNnQkdHhwlRaSfrPkAMwzAMwzCik/MlMIZhGIZhGFHhAohhGIZhmJKDCyCGYRiGYUoOLoAYhmEYhik5yBRAGzZsgGXLlsGyZctg8eLFsGbNGk3fNcYwDMMwpcz27dth8eLFZochHCQeyl+8eDG4XC7o7e1VHp+vq6uDhQsXwt69exO+Y4RhGIZhSp2+vj648soroa+vj/vu5YDpZ4Dq6uqgr68POjs7Y3ZgZ2cnAAAsXbrUpMgYhmEYhiYbNmyA++67z7ArJam+6qqYMLUPUF9fHyxevBiqqqqSfrHqmjVroL29Hdra2vhrNdLgcrlg3bp18MILL8CSJUvA4XBAVVUVuFwuaGtrMzs8YeFxZQD4c2AkPLb5097eDmvWrAGLxQJOp1MXZ19fH9TV1aX8wvNiwdRLYLfccgsAANTU1CR9f9myZdDe3g4tLS1cAKWhrq4Ouru7wel0Kh25Fy5cyF9Gmyc8rgwAfw6MhMc2fxYsWGB2CMJiagEkn2JLdfOWfO/P4OAgDA4OQlVVVcFiEwl5HOVLiPIX0UaPV19fH3R3d8PAwACsWbOG76vSAI8rA6Dtc+ByueCFF16Avr4+qKmp4c+BRnhsGTMx7R6gwcFB5dplqsImurLt6+srRFhCs337duXfK1asiDlQVFVVwYEDB6C9vd2wA8jg4CDU1dXBmjVrYM2aNbBu3TpDfk6hMXtcZdrb22HZsmWG/gwmNek+Bw6HA9ra2gz7zBf7vjdjbIv1eEWBvr4+5dLc4sWLob29XXlv3bp1MH/+fOXhJ5nt27fDokWLYP78+QW7/8jUAkgm1Sm86Juio5dnYpEvD1555ZXKB6q6ujqmsJRPLxtZ/CxevBiWLVsGbW1t0NbWBi6XS+iDNoVxBZB+S163bh2sWbOG54EJaPkcVFVVQVVVFVgsFl0/C8W+780a22I8XlHD5XJBe3u7cuYOAJTxXbBggXL2HEAqiu677z6orq4Gl8sFLS0thQkSTaKzsxMBAAEABwYGUi4nL9Pc3FzA6MTC6XSixWJBAMCampqUyxk5jrW1tWixWBLiAgBsa2sz5GcaDYVxjaaqqgqrqqoM/zlMLFo/B1VVVVhbW2tIDMW6780a22I6Xsm5NH578qG3tzfvz1tvby8CgOJpampScn1NTQ0CAHZ1dWFbW5sy5vK2pPssyHR1dWFLS0uMN1tMfwweANI+xiefBeKmiKmxWCxwxx13AID0G2P06WQZ+RLiihUrdP/5LpcLtm/fDvX19QlxVVVVFa6a1xmzx5WhgZbPgcvlgsHBQT6DkCVmjG2xHq+oIZ/dqampgQ0bNsC6deuUM3svvPCCspzL5VLOBHZ1dQEAaNrXNTU1yu0Hud4fbFoBFH15y+FwpFxOLny4yVMi0UVhbW2tcppRfrouGvnDKJ9irKurg/nz58cccDZs2AB1dXWwaNEiAADloJPpmqz8nrxeNNXV1TH3e4kAlXFlzCWXz0GqJ1rlx73XrVundL0vxktaWjFzbIvteEUVuZiRC0u5SOnr61NuG+js7ITm5mZlnUz7Op68bz/I6byRDsinGwEAOzs7Uy4nLyPaaUmjGRgYSLjsIp9yTLZba2pqsKamBp1OJzY1NaHT6cSqqqqEU40Wi0VZrrm5WdlPTU1NKWNpaWlJuY+ampoQALC3tzfHLS0slMY1nmK9DEKRbD8HTU1NKS9ByJcTurq6lNdqamqy2pfFtO/NHttiOl4h5nYJLPoWlFz/ZPp58nLxxzh5/C0WS8w4y8fEbLYD8rz9wNQzQPJZnVRngKKreK0VYakwODiYcPZAroSTnQ7s7u6GZcuWwS233AJtbW1gsViSthaQbwSU+y/JJPttScZutwNA+n4U6c7yUYLSuDLmkcvnINkxyuVywdKlS2POcsiuUj3TYPbYFtPxKldqa2sBEVP+6e3thaqqqrTLpGu6GL1/45+uk88Mxbc0yPbsj7x8PpdGTb0HSL4Gm6rbpFwAyafQGJXBwUHo6+uLudQSfUd9NPLrAwMDSoMx+d6Vurq6hOXsdrtyTVa+VltbW5syFjmJpztoiNKsi9K4MuaRzedAvkcl2X1gdXV14HK5Ehr7lfLlL7PHtpiOV1SJLnLic7e8r+PvtdJy/8+aNWugrq4O6urqlK/LyufkiKmNEOWvukh1H4Q8INwVNBGXywXNzc3Q1dUFBw4cUJqFdXZ2JiRVeRzr6uqUs27Jqm35A/WJT3xCWa6rqytjAbpkyRIlpnjkg4woBSylcWXMI5vPQarfXF0uF3R3d0NTU1PCPYx9fX3Ko92lhtljW0zHK6rI+23NmjVJX49vdRD9nnxixOVyxTwEtXjxYmhpaYHa2lpwuVwwf/78/K8M5XzxTCfkx+GSPcZmsVjQYrGg0+ksfGBFRHV1dcI18erqaqyuro55Ldl9BloeP013P0uyn10sGD2u8RTTfSDFRG1tbdL9It9nEX+vSVdXV8Z7H+Mp1X1vxNgW2/GK2mPw0ff3xtPc3IwAgC0tLUnXkX9mW1ubcl+X3Cohen8NDAwk9WSL6Y/Bd3Z2QnV1tXI6U0auHPfu3VuSvyXpSV9fX0Il3tfXp5xWdrlcyqnm6OWiHz+Vl0mGxWKB2trahDN5Lpcr6c8uFoweV0YMou9Rie5YL59NkM84yMi/xfLlz8wYMbbFdrw6cOAAANBpFSOPa7J9kOqMXvRj8Rs2bIhZRr4sGn3JTL7Mme8cMr0Aslgs0NvbCytWrFCu7cnXAN98803+3pc8SfaBkw8k1dXVsGHDBrBYLHD//fcDQOwHSv5QLlmyBNrb25VCdHBwEMrKymKu17e0tIDD4Yi5rn/LLbdAVVVVzGOOxUKhxjWeYr85UzTkm23lG9yjT+vLn43og7v8lQDyZdFoD+/7WIwc22I4Xm3fvl159F9m2bJlsGHDBlPvMRscHITq6uqUvdGSfZ/bkiVLlONgVVWVcq/k4OAgtLe3w3XXXRdzIqSzs1OfWwjyOn/EkKelpSXhkoz8yKn8WDaidGoy/tHt3t5etFgsCZdq5FOS8V6n04m1tbXY3NyMTU1NymPhxUghx7W3t1c5dQxHT/uK9JhusSPvs2SX8Ts7O7G6uhpbWlpiOt7Gw/s+OUaObSkdr7JFj07QeiB/9qP3fy4tRFLBBRCTM/lef2WSw+NauvC+Nw4eW+0MDAxo+joKo6mtrU24l0ju1STf55VP0Wr6JTCGYRiGYehQVVWlPOVqJnI7AvlS5YYNG5Q2BhaLJeYWglww9TF4RlzkR00ZfeFxLV143xsHj62YtLS0wAsvvAB1dXVQU1MDbW1tyuPxnZ2d0NbWlpefCyAmJ7q7u4W5WVAkeFxLF973xsFjKybyQ1LxpOtCnQ1liIi6mBiGYRiGYQSB7wFiGIZhGKbk4AKIYRiGYZiSgwsghmEYhmFKDi6AGIZhGIYpObgAYhiGYRim5OACiGEYhmGYkoMLIIZhGIZhSg4ugBiGYRiGKTm4AGIYhmEYpuTgAohhGIZhmJKDCyCGYRiGYUqO/w8yfuL0mjCozwAAAABJRU5ErkJggg==",
      "text/plain": [
       "Figure(PyObject <Figure size 600x600 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject Text(0.7, 0.03, '\\\\shortstack[l]{Asset supply, $s$}')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Figure 7(b): high y_d (under low ρ)\n",
    "\n",
    "## range of asset supply\n",
    "mD = 0.1 # dealer size\n",
    "xRange = range(1e-3,1.0+mD-1e-3,length=1000)\n",
    "\n",
    "## initialize output\n",
    "di = Dict()\n",
    "\n",
    "## compute\n",
    "guess=[0.99,0.01,] # set some initial guess\n",
    "for x in xRange\n",
    "    diPara = Dict(\n",
    "        :s  => x,:mD => 0.1,\n",
    "        :λu => 0.1,:λd => 0.1,:r=>0.05,\n",
    "        :values => (1.0, 0.9, 0.0), # yh, yd, yl\n",
    "        :searchB => (1.0, 1, 0.5), # ρB, nB, qB\n",
    "        :searchS => (1.0, 5, 0.0), # ρS, nS, qS\n",
    "    )\n",
    "    converging,θLO,θHN,diB,diS,diX = solveEquilibrium(diPara,guess=guess)\n",
    "    diX[:plannerθLO], diX[:plannerθHN], diX[:plannerWelfare] = plannerTechChoice(diPara)\n",
    "    di[x] = Dict(\n",
    "        :converging=>converging,\n",
    "        :θLO=>round(θLO,digits=5),:θHN=>round(θHN,digits=5),\n",
    "        :diB=>diB,:diS=>diS,:diX=>diX\n",
    "    )\n",
    "    guess = converging ? [θLO,θHN,] : rand(2) # recursively update guess based on the previous eqm\n",
    "end\n",
    "\n",
    "## find thresholds for s\n",
    "s1N  = findlast([di[x][:θHN] for x in xRange] .== 0.0);   s1 = xRange[s1N]\n",
    "s2N  = findfirst([di[x][:θHN] for x in xRange] .== 1.0);  s2 = xRange[s2N]\n",
    "s2pN = findlast([di[x][:θLO] for x in xRange] .== 1.0);  s2p = xRange[s2pN] # p for planner\n",
    "s1pN = findfirst([di[x][:θLO] for x in xRange] .== 0.0); s1p = xRange[s1pN] # p for planner\n",
    "\n",
    "## set up canvas\n",
    "fig = PyPlot.figure(figsize=(5,5), facecolor=\"w\", dpi=120) # create figure\n",
    "fig.subplots_adjust(left=.1, right=.92, bottom=0.12, top=.9) # reduce white spaces\n",
    "ax = fig.add_subplot(111) # create axis for total trading cost\n",
    "\n",
    "## plot thetas\n",
    "ax.plot(xRange, [di[x][:converging] ? di[x][:θHN] : NaN for x in xRange], ls=\"-\", color=\"blue\")\n",
    "ax.plot(xRange, [di[x][:converging] ? di[x][:θLO] : NaN for x in xRange], ls=\"--\", color=\"orange\")\n",
    "\n",
    "## vertical lines\n",
    "ax.vlines([s1,s2,s2p,s1p],ymin=0.0,ymax=1.0,linestyle=\":\",lw=0.8,colors=\"gray\")\n",
    "\n",
    "## fill\n",
    "ax.fill_between([s1,1.0+mD],[1.0,1.0], facecolor=\"none\", hatch=\"//\", edgecolor=\"blue\", linewidth=0.0)\n",
    "ax.fill_between([s2p,1.0+mD],[1.0,1.0], facecolor=\"none\", hatch=\"\\\\\\\\\", edgecolor=\"orange\", linewidth=0.0)\n",
    "\n",
    "## set axes ticks and ranges\n",
    "xmin, xmax = 0.0, 1.0 + mD\n",
    "ax.set_xlim([xmin, xmax])\n",
    "ax.set_ylim([-0.005,1.05])\n",
    "ax.set_yticks([0,1])\n",
    "ymin, ymax = ax.get_ylim()\n",
    "ax.set_xticks([0.0,s1,s2,s2p,s1p,1.0+mD])\n",
    "ax.set_xticklabels([\"0\",L\"$s_{hn,0}$\",L\"$s_{hn,1}$\",L\"$s_{lo,1}$\",L\"$s_{lo,0}$\",L\"$1+m_d$\"])\n",
    "\n",
    "## reference lines\n",
    "ax.hlines([0.0,],xmin=minimum(xRange),xmax=1.0 + mD,linestyle=\":\",lw=0.8,colors=\"gray\")\n",
    "\n",
    "## format axes\n",
    "ax.spines[\"right\"].set_visible(false)\n",
    "ax.spines[\"top\"].set_visible(false)\n",
    "arrowed_spines(ax) # add arrows\n",
    "\n",
    "## label axis next to arrows\n",
    "ax.annotate(L\"\\shortstack[l]{$\\theta_{hn}$ (solid line) and $\\theta_{lo}$ (dashed line)}\", xy=(0.03,1.0,), xycoords=\"axes fraction\") # left y label\n",
    "ax.annotate(L\"\\shortstack[l]{Asset supply, $s$}\", xy=(0.7,0.03), xycoords=\"axes fraction\") # x-axis label"
   ]
  }
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